In our rapidly evolving digital landscape, the velocity of information exchange has reached unprecedented heights. While this immediacy offers numerous advantages, it also presents challenges for our capacity for deep, reflective thought and meaningful dialogue. This article delves into the necessity of decelerating our conversations to cultivate a more thoughtful society, examining the unconscious processes influencing our decisions, the pervasive impact of social media, the enduring wisdom of ancient Greek philosophy, and the imperative to create spaces for deliberate, face-to-face interactions as we look to the communities and societies we want to build for our future.

Human beings like to believe they are rational thinkers, carefully considering facts before making decisions. However, research suggests that the vast majority of our choices are made unconsciously. Nobel Prize-winning psychologist Daniel Kahneman, in Thinking, Fast and Slow, highlights how we make thousands of unconscious decisions daily—ranging from simple motor movements to complex social judgments—without ever engaging in active, conscious thought.

It is this ability for speedy and prolific unconscious processing that has provided our human ancestors with an evolutionary advantage. Early humans needed to react quickly to threats, whether escaping predators or seizing an opportunity for food; determining which group of people is more likely to help us or kill us. Conscious thinking, on the other hand, is slow and effortful, consuming valuable mental energy. Studies in cognitive neuroscience confirm that engaging in deliberate, rational thought depletes resources. Research published in Trends in Cognitive Sciences found that the brain’s prefrontal cortex, responsible for conscious reasoning and decision-making, consumes a disproportionately high amount of energy compared to other cognitive functions[1].

While unconscious decision-making has helped humans survive, it also makes us vulnerable to manipulation. In today’s digital world, where we are constantly bombarded with information, advertising, and political messaging, much of our behaviour is shaped without our realising it. Social media platforms, for example, use sophisticated algorithms to target our unconscious biases, reinforcing existing beliefs and shaping our perceptions of reality.

This is why slowing down conversations is more critical than ever. When we take time to reflect rather than react, we engage our higher cognitive processes, allowing for greater discernment and more meaningful discussions. The challenge is that such effortful thinking requires energy, and in a world that constantly demands our attention, we often default to quick judgements and emotional responses.

To counteract this, we must create opportunities to consider ideas thoughtfully rather than reacting to them unthinkingly, opportunities to step away from the fast-paced, reactive nature of digital discourse and engage in more deliberate, in-person discussions. A thinking society is one that values reflection over reaction, ensuring that we are not simply being swept along by unconscious influences but actively shaping our collective future.

How Social Media Shapes Our Perception

In our hyper-connected world, social media platforms have become integral to daily life, offering unprecedented access to information and diverse perspectives. However, this constant exposure also subjects us to an unending stream of opinions, influences, and propaganda—often without our conscious awareness. These platforms are designed to keep us engaged, and do so by putting us metaphorically in a warm bath of acceptance and confirmation, reinforcing our existing beliefs and limiting exposure to differing viewpoints, ultimately leading to a distorted perception of reality.

The concept of filter bubbles, introduced by Eli Pariser, describes how algorithms curate content based on our past behaviour, effectively isolating us from opposing perspectives. This phenomenon is not merely theoretical; research has demonstrated its tangible effects. A study published in Applied Network Science[2] used agent-based modelling to show how social media’s filter bubbles and echo chambers contribute to societal polarisation by limiting the diversity of information users encounter.

While some argue that concerns about filter bubbles may be overstated, evidence suggests otherwise. These digital silos amplify specific messages and suppress alternative views, creating a skewed understanding of the world. A 2023 study in Nature Communications[3] found that individuals exposed primarily to one-sided political content on social media exhibited a stronger reinforcement of pre-existing beliefs, demonstrating the power of these algorithms to shape public opinion.

The implications of this curated reality are profound. By continually interacting within these algorithmically constructed environments, our beliefs become more entrenched, and our openness to differing opinions diminishes. This fragmentation poses a significant challenge to social cohesion, as shared understanding becomes increasingly elusive.

To mitigate these effects, it is crucial to cultivate awareness of how social media platforms shape our information landscape. Actively seeking out diverse perspectives, following sources outside of our usual media diet, and engaging in open, real-world dialogues can help counteract the narrowing influence of filter bubbles. By doing so, we can foster a more informed and empathetic society, better equipped to navigate the complexities of our interconnected world.

Why Real Conversation Matters More Than Ever

Despite being constantly connected through technology, genuine face-to-face interactions are diminishing, leading to a decline in effective communication skills. This trend raises concerns about our ability to engage in thoughtful discourse and critically evaluate differing viewpoints.
However, recent studies indicate a worrying decline in communication skills. Research published in Discourse Studies[4] highlights how digital communication has altered interpersonal exchanges, reducing individuals’ ability to engage in deep, meaningful conversations. The study found that reliance on short, text-based interactions has weakened people’s capacity for extended verbal reasoning.

The implications of this decline are profound. Without strong communication skills and the ability to reason effectively, individuals may struggle to navigate complex social and political landscapes. This deficiency can lead to increased polarisation, as people become less capable of understanding and valuing perspectives different from their own.

To address this issue, it is imperative to prioritise the development of communication skills and critical thinking. Educational institutions and workplaces should emphasise the importance of face-to-face interactions and provide opportunities for individuals to practice and refine these skills. By doing so we can foster a society that values thoughtful conversation, respects diverse opinions, and engages in rational discourse.

The ancient Greeks placed immense value on the art of conversation, reasoning, and argumentation. Philosophers like Socrates, Plato, and Aristotle developed foundational principles of logic to guide rational discourse which was essential for law, societal ethics, politics and personal morals. It was seen as necessary to be able to understand their world, to distinguish truth from falsehood and live a virtuous life.

Socrates is widely recognised for developing dialectical reasoning, a method of questioning and dialogue designed to stimulate critical thinking and expose contradictions in people’s beliefs and logic. This approach was central to his philosophy, particularly in his search for ethical truths. In dialogues like Plato’s “Euthyphro”[5], Socrates asks probing questions that encourage his audience to reconsider their assumptions. He often employed the reductio ad absurdum technique to expose contradictions, demonstrating that beliefs, when fully examined, often led to illogical conclusions.

Socrates argued that ethical knowledge required rigorous reasoning and that identifying “logical fallacies”[6]—such as ad hominem attacks or false dichotomies—was critical for understanding morality and achieving wisdom. His method of questioning revealed how illogical thinking undermines ethical behaviour, emphasizing that clear reasoning is essential for virtue. Socrates sought universally applicable ethical principles, moral truths that would hold regardless of the personal or societal context in which they are applied.

Aristotle’s ethical philosophy, as outlined in his Nicomachean Ethics[7], expands on Socratic ideas by linking the concept of human flourishing (eudaimonia) ultimately to that of personal virtuous activity which included living a life of reason and wisdom. The actions of the individual and how they aspire to live virtuously, wisely and with reason, was viewed by Aristotle as both productive of, and supported by, a well-governed and well-structured society. A society made up of virtuous, responsible citizens is one that functions harmoniously and justly, fostering a collective well-being that surpasses individual pleasure. Thus, individual and collective flourishing are deeply intertwined.

Look Up

“Words are only a small part of what we communicate”[8]. In today’s digital age, our constant engagement with smartphones has significantly reduced face-to-face interactions, leading to a decline in meaningful conversations. This shift not only affects the depth of our relationships but also undermines essential communication skills.

A substantial portion of human communication is non-verbal, encompassing facial expressions, gestures, posture, and eye contact. These cues play a pivotal role in conveying emotions and intentions. While exact figures vary, it’s widely acknowledged that non-verbal elements significantly influence our understanding during interactions. For instance, maintaining eye contact can indicate confidence and sincerity, whereas crossed arms might suggest defensiveness or discomfort. Being attuned to these signals enhances our ability to connect authentically with others.

In his book, “The Anxious Generation”[9] social psychologist Jonathan Haidt discusses how the rise of digital communication has impacted traditional social skills. He observes that children today have fewer opportunities to engage in unstructured, face-to-face play, which historically allowed them to navigate conflicts and develop reconciliation skills. Haidt notes, “The decline of free play means kids are less likely to have minor disputes and then work them out, a crucial process for building resilience and social competence.”

To counteract these trends, it’s imperative to consciously prioritise real-life interactions. Engaging in face-to-face conversations allows us to fully experience the richness of human communication, fostering empathy and understanding. “Face-to-face communication remains the richest form of interaction, where both verbal and nonverbal channels are fully engaged, mediated communication can never fully replicate the depth of understanding facilitated by face-to-face encounters.”[10] When in-person meetings aren’t feasible, video platforms serve as valuable alternatives, enabling us to observe visual cues and maintain a personal connection.

By looking up from our phones and embracing direct communication, we can rebuild essential social skills, strengthen relationships, and enhance our overall well-being.

Why We Must Slow Down the Conversation

In our fast-paced digital era, social media platforms inundate us with a relentless stream of posts, opinions, and notifications, fostering a culture of instant gratification. This environment often prioritises immediate reactions over thoughtful deliberation, leading to superficial engagement rather than meaningful conversations. Social media platforms are designed to provide immediate rewards—likes, comments, shares—that trigger dopamine release in the brain, reinforcing the desire for continuous engagement. This cycle encourages users to seek quick validation, diminishing patience for more substantial, time-consuming interactions. Such behaviour aligns with the concept of “popcorn brain,” where individuals develop fragmented attention spans due to constant digital stimulation[11].

The emphasis on rapid responses and the constant comparison facilitated by social media can adversely affect mental health. A systematic review published in BMC Psychiatry found that excessive use of social networking sites is associated with increased risks of depression, anxiety, and psychological distress[12]. Another study in Frontiers in Psychology highlighted how smartphone-based social networking service use leads to lower life satisfaction and heightened emotional distress, reinforcing the need to disconnect and engage in more intentional communication[13].

In this context, it becomes essential to consciously slow down our conversations. Taking time to reflect before responding allows for deeper understanding and more meaningful exchanges. Engaging in face-to-face interactions or extended dialogues encourages empathy, critical thinking, and the ability to navigate complex issues—skills that are often underdeveloped in the realm of instant messaging. To foster a more thoughtful society, we must create spaces that encourage deliberate conversation. This involves setting boundaries for digital device usage, promoting environments conducive to in-depth discussions, and valuing the quality of interactions over quantity. By doing so, we can move beyond the superficiality of instant reactions and work towards healing societal rifts, improving our communities, and envisioning a future where all individuals have the opportunity to flourish.

The Societal Cost of Closing Down Free Speech

A free society depends on the open exchange of ideas. When free speech is curtailed, and citizens can no longer discern truth from falsehood, the consequences ripple across communities, institutions, and governance. The suppression of honest conversation weakens democracy, erodes trust, and ultimately diminishes society’s ability to self-correct and evolve. The ancient Greeks understood that truth is best revealed through debate and discourse. Socrates’ method of questioning—elenchus—was designed to challenge assumptions, refine arguments, and pursue knowledge. Aristotle further developed this idea, arguing that free citizens must engage in reasoned discussion to achieve eudaimonia—human flourishing[14].

John Stuart Mill, in On Liberty[15], warned of the dangers of silencing dissent, arguing that “the peculiar evil of silencing the expression of an opinion is…robbing the human race… If the opinion is right, they are deprived of the opportunity of exchanging error for truth: if wrong, they lose, what is almost as great a benefit, the clearer perception and livelier impression of truth, produced by its collision with error”.

When open discourse is restricted, institutions that rely on free inquiry—such as education, the judiciary, and the press—begin to falter. In education, students may be taught not to think critically but to conform to prevailing ideologies, leading to intellectual stagnation. Studies have shown that environments where dissent is discouraged produce lower-quality scholarship and innovation.[16]

The judiciary, an institution tasked with impartial judgment, also suffers when free speech is curtailed. Without open debate and the ability to challenge laws and policies, the legal system risks becoming a tool of oppression rather than justice.

At the community level, restricting speech creates social fragmentation. Without open discussion, differences are not worked through but suppressed, leading to increased polarisation. Research on political suppression indicates that societies that discourage free speech experience greater civic disengagement and a decline in institutional trust.[17]

Governments that limit speech often justify it under the guise of public order or misinformation control, but history shows that suppressing dialogue rarely leads to stability. Instead, it breeds underground radicalization, as individuals who feel unheard seek alternative, often extreme, ways to express their views.

Valuing free speech does not mean endorsing all opinions as equally valid, but it does require creating a space where ideas can be tested and refined. The challenge today is to resist both state and cultural pressures to conform unthinkingly to the opinions that happen to be in vogue, and instead foster environments where truth is pursued through discussion, debate, and intellectual humility.

By looking back to the wisdom of the Greeks and the insights of Mill, we see that protecting open discourse is not just about preserving speech but about preserving the very foundation of a free, self-governing, and rational society.

Embracing Thoughtful Engagement for a New Society

In an era dominated by rapid communication and fleeting interactions, the need for thoughtful engagement has never been more pressing. The pace of modern life—accelerated by social media and digital platforms—often leaves little room for careful reflection, critical thinking, and meaningful dialogue. To build a society that values understanding, diversity, and genuine connection, we must intentionally slow down our conversations and create spaces for civil discourse.

Civil discourse is more than just polite conversation; it is the foundation of a functioning democratic society. It involves engaging in discussions that prioritise mutual respect, active listening, and openness to differing viewpoints. Studies in political psychology suggest that societies with strong norms of deliberative discourse are more resilient to misinformation and political polarisaion[18]. Such discourse encourages individuals to critically evaluate information, consider alternative perspectives, and engage constructively—even amidst disagreement.

In The Polycentric Republic: A Theory of Civil Order for Free and Diverse Societies[19], David Thunder explores how societies can be structured around multiple centres of power and influence, rather than being dominated by a single centralised authority. He argues that a polycentric approach allows for a richer, more diverse civic life, and requires citizens who are capable of critical thinking, respectful dialogue, and meaningful engagement with differing perspectives. Without these skills, diversity becomes a source of division rather than strength.

For a polycentric republic to function effectively, its citizens must engage in thoughtful and meaningful conversations, navigating the complexities of diverse viewpoints to collaborate across different societal sectors. This is in stark contrast to existing communication habits as discussed previously and summarised in a study in New Media & Society which confirmed that social media engagement encourages polarized and knee-jerk reactions rather than measured, reflective discussion.[20]

To counteract this, we must deliberately create space and time for slower, reflective conversations. Studies show that in-person interactions lead to greater trust and empathy compared to text-based communication.[21]

To cultivate a society that embraces thoughtful engagement, we must prioritise:

• Active Listening: Genuine engagement with others’ viewpoints, resisting the urge to immediately counter or dismiss their ideas.

• Open-Mindedness: Willingness to reconsider one’s beliefs in light of new information or persuasive arguments.

• Respectful Dialogue: Approaching conversations with civility and patience, even when disagreements arise.

• Critical Thinking: The ability to analyse claims, distinguish between strong and weak arguments, and recognize biases in both ourselves and others. Research has shown that individuals with strong critical thinking skills are less susceptible to misinformation and manipulation[22].

• Encouragement of Diverse Opinions: Valuing and seeking out differing viewpoints to enrich discussions and foster innovation.

By embracing these principles, we can build a society that not only tolerates diversity but actively thrives on it. Fostering civil discourse, critical thinking, and deep engagement, we can reclaim the art of conversation and build a future where individuals and societies can flourish.

References

1. Raichle, M. E., & Gusnard, D. A. (2002). Appraising the Default Mode: Implications for Consciousness and Decision-Making. Trends in Cognitive Sciences, 6(5), 206-214.

2. Kaiser, J., & Rauchfleisch, A. (2024). Echo chambers, filter bubbles, and polarisation: How social media shapes public discourse. Applied Network Science, 9(1), 45-62.

3. Bail, C., Argyle, L., Brown, T., & Yang, J. (2023). Exposure to opposing views on social media can increase political polarisation. Nature Communications, 14(2), 1129-1144.

4. Tagg, C. (2021). “Digital Communication and the Decline of Extended Discourse.” Discourse Studies, 23(2), 189-207.

5. Plato. (1997). Euthyphro. In E. Hamilton & H. Cairns (Eds.), The Collected Dialogues of Plato (pp. 1-17). Princeton University Press.

6. Hansen, H. V. (2020). Fallacies and Argument Appraisal, Cambridge University Press.

7. Aristotle. (2002). Nicomachean ethics (W. D. Ross, Trans.). Batoche Books. (Original work published ca. 350 BCE)

8. Mehrabian, A. (1971). Silent Messages: Implicit Communication of Emotions and Attitudes. Wadsworth Publishing Company.

9. Haidt, J. (2024). The Anxious Generation: How the Great Rewiring of Childhood Caused an Epidemic of Mental Illness. Penguin Random House.

10. Lapakko, D. (2007). Communication is 93% Nonverbal: An Urban Legend Proliferates. Communication and Theater Association of Minnesota Journal, 34, 7-14.

11. Moawad, H. (2021). “Popcorn Brain: How Constant Digital Stimulation is Changing Our Thinking.” Neuroscience Journal, 45(3), 112-129.

12. Keles, B., McCrae, N., & Grealish, A. (2020). “The Impact of Social Media on Adolescent Mental Health: A Systematic Review.” BMC Psychiatry, 20(1), 348.

13. Wang, X., & Gao, X. (2023). “Social Media and Psychological Well-Being: The Role of Smartphone Overuse.” Frontiers in Psychology, 14, 985958.

14. Aristotle. Politics, trans. Ernest Barker. Oxford University Press, 1998.

15. Mill, J. S. (1859). On Liberty. London: John W. Parker and Son.

16. Lukianoff, G., & Haidt, J. (2018). The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure. Penguin Press.

17. Inglehart, R. (2018). Cultural Evolution: People’s Motivations Are Changing, and Reshaping the World. Cambridge University Press.

18. Kunda, Z. (1990). The Case for Motivated Reasoning. Psychological Bulletin, 108(3), 480-498.

19. Thunder, D. (2023). The Polycentric Republic: A Theory of Civil Order for Free and Diverse Societies. Oxford University Press.

20. Bail, C. A., et al. (2021). Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing. Princeton University Press.

21. Derks, D., Fischer, A. H., & Bos, A. E. (2008). The Role of Emotion in Computer-Mediated Communication: A Review. Computers in Human Behaviour, 24(3), 766-785.

Pennycook, G., & Rand, D. G. (2019). The Implied Truth Effect: Attaching Warnings to a Subset of Fake News Stories Increases Perceived Accuracy of Stories Without Warnings. Management Science, 65(11), 4944-4957.

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Negli ultimi anni ho imparato tante cose nuove, una delle poche, forse l'unica, caratteristica redentiva della “pandemia”.

Queste lezioni si estendono a molte discipline: tecnologia, immunologia, virologia, epidemiologia, statistica, filosofia, teoria politica e diritto pubblico, per citarne alcune. Tuttavia, una disciplina che mai avrei pensato sarebbe stata rilevante per analizzare gli eventi recenti è la geografia. Era una materia che detestavo a scuola, anche se col senno di poi probabilmente non sono il solo a concludere che la propensione per una determinata materia dipenda più dal fatto che piaccia l’insegnante, che da una reale inclinazione personale.

In ogni caso, sembra che oggi la geografia sia molto più matematica di quanto non fosse quando la studiavo più di quarant’anni fa. L’ho scoperto grazie a un biologo evoluzionista di PANDA che adotta un approccio laterale e mi passa informazioni, supportate da prove, che molti considererebbero eretiche. Tuttavia, non desidera apparire sotto i riflettori. È lui che ha eseguito i calcoli su cui si basa questa analisi delle morti in eccesso in Lombardia.

Torniamo alla geografia e alla sua matematica associata. Alcune delle domande che chi studia geografia tende a porsi sono: in che misura e perché diversi luoghi sono simili o diversi? Quale processo li ha resi tali e dove e quando potrebbe essere iniziato quel processo?

Questo ha un'applicazione ovvia nell'analisi della presunta diffusione di un nuovo virus letale nel mondo a partire da Wuhan, come sostenuto dalle autorità nei primi mesi del 2020. Come indicato in diverse analisi (vedi, ad esempio, questi studi con dati da Italia, USA, Congo e Brasile), ci sono sempre più prove della presenza totalmente inosservata del virus già prima della data ufficiale dell'inizio della pandemia, persino già da settembre 2019. In quasi tutti gli studi che riportano questi dati, sembra essere stata trascurata la rilevanza dell'assenza di mortalità in eccesso fino alla dichiarazione di emergenza.

Vale la pena considerare questo controfattuale: immagina che non ci fosse nessun virus, ma che per qualche altra ragione (qualunque essa sia) i governi decidessero di adottare una serie di misure, tra cui:

1. Dire alle persone di non recarsi in strutture sanitarie se presentavano sintomi come tosse o febbre, sia per “proteggere” il sistema sanitario, sia perché qualsiasi contatto con esso avrebbe potuto far contrarre una malattia mortale.

2. Dire al personale sanitario di isolarsi se loro stessi (o in alcuni casi, qualcuno nella loro famiglia) avessero ricevuto un test positivo per una determinata malattia, anche se asintomatici.

3. Svuotare i letti in preparazione per un imminente “sovraccarico”.

4. Terrorizzare e isolare le persone anziane, in particolare quelle che vivono in case di cura, negando loro le visite dei familiari e riducendo o eliminando le visite in persona da parte di assistenti sociali e sanitari.

5. Utilizzare l'intera macchina statale più tutti i canali social e i media tradizionali per promuovere una narrazione di paura esagerata rivolta al pubblico, con ripercussioni anche sugli operatori sanitari, nonostante sia ben noto che lo stress abbia diversi effetti negativi sulla salute, tra cui l'immunosoppressione.

6. Ricorrere in modo massiccio a un trattamento (ventilazione) senza una solida base di prove, ora noto per essere estremamente dannoso.

L'implementazione di tali politiche avrebbe provocato proteste per le strade con persone che dichiaravano che "migliaia di persone moriranno sicuramente", e con ogni probabilità avrebbero avuto ragione. È inconcepibile che tali politiche non avrebbero avuto una mortalità significativa associata. Ricorda come un ex Ministro della Salute britannico, Jeremy Hunt, si sia detto preoccupato per il fatto che le (relativamente più lievi) carenze del personale NHS durante il fine settimana aumentassero misurabilmente i rischi per i pazienti sfortunati da ammalarsii fuori dall'orario lavorativo.

Dovrebbe quindi essere ragionevole supporre che almeno alcune delle morti avvenute a seguito dei cambiamenti catastrofici nell'erogazione delle cure sanitarie, soprattutto per i fragili e gli anziani, possano essere state causate da politiche, piuttosto che da un virus. La domanda è: quale proporzione è stata causata da tali cambiamenti politici e quale dalla diffusione di un virus nella popolazione?

Il punto di partenza per analizzare questa questione è chiedersi: qual è la prova che la diffusione di un virus sia la causa delle curve di decessi in eccesso osservate? La “diffusione” può essere misurata, e quali sarebbero le implicazioni di risultati diversi?

Immaginiamo un incendio in una foresta che inizia in un angolo di un bosco secco, forse innescato da qualcuno che ha lasciato un barbecue fumante incustodito. Si avvierebbe con un unico cluster localizzato di fiamme, che poi crescerebbe e si diffonderebbe in filamenti finché non si incontra una zona con più combustibile secco. Queste aree a loro volta prenderebbero fuoco, propagando le fiamme forse attraverso il contatto diretto con le zone vicine. Di tanto in tanto, una scintilla potrebbe volare via o un ramo morto in fiamme potrebbe cadere da un albero, accendendo un’area leggermente più lontana, e il processo continuerebbe da lì. Dopo un po', l’intera foresta sarebbe in fiamme, ma solo per un breve periodo, perché il fuoco si estinguerebbe rapidamente da sé, con le varie aree che si spegnerebbero in tempi diversi poiché gli incendi non sono cominciati in tutte le zone contemporaneamente.

Questo è ciò che ci si aspetterebbe di osservare quando un processo si diffonde a partire da una fonte puntuale. Quello che NON ci si aspetterebbe di vedere è l’intera foresta che prende fuoco contemporaneamente, con tutte le aree che si esauriscono simultaneamente. Se ciò accadesse, la maggior parte delle persone assumerebbe che fosse successo qualcosa che ha interessato tutta l’area nello stesso momento — e che non dipendesse affatto dalla sua diffusione — forse una gigantesca palla di fuoco distruttiva derivante da un'esplosione vicina.

Un punto chiave in relazione a questo è che analizzare la scena dopo l’evento non è realmente utile per determinare la causa. In entrambi gli scenari il risultato appare simile: una foresta bruciata. È necessario guardare una serie temporale, ossia come le diverse aree sono state colpite nel tempo, per trovare prove conclusive della diffusione.

Diamo un'occhiata di nuovo alle curve di decessi totali nelle 13 aree amministrative (d'ora in avanti indicate come “province” o “aree amministrative”) che compongono la regione Lombardia.

Questi dati non sono affatto quelli che ci si aspetterebbe da un processo di diffusione di un patogeno. Dalla semplice osservazione emerge che un processo quasi simultaneo, indipendente dallo spazio, ha agito nella regione della Lombardia. Un'analisi dettagliata rivela che le curve di decessi in eccesso per Lodi sembrano iniziare intorno al 23 febbraio e, per tutte le altre aree, ciò avviene il 1° marzo o nei pochi giorni successivi.

Ma è possibile dimostrarlo in modo più matematico?

La risposta è sì, utilizzando la misurazione dell'autocorrelazione. Si tratta essenzialmente di un'analisi statistica delle caratteristiche delle aree vicine, che genera diverse statistiche (la più nota delle quali è il "Moran’s I"), le quali indicano fino a che punto tali caratteristiche dipendono spazialmente — ossia, quanto il valore di una caratteristica in una data area dipende dal valore della stessa caratteristica in un'area adiacente.

La caratteristica che stiamo esaminando qui è l’eccesso di mortalità. È assiomatico che un virus letale che si diffonde in una popolazione causerà decessi in eccesso se il suo impatto si aggiunge alle vicissitudini usuali della vita (e della morte). Per questo motivo, questa è una misura adeguata — e decisamente migliore del conteggio dei decessi etichettati come “decessi covid”, poiché elimina la variabilità introdotta dalle differenze nelle politiche di test, dai test inaffidabili e dalle decisioni dei medici sulla causa del decesso.

Come evidenziato nell'analisi sulla Lombardia, siamo fortunati a disporre di dati estremamente dettagliati sui decessi giornalieri per l’Italia; infatti, questi sono probabilmente i dati più granulari disponibili al mondo, mostrando i decessi giornalieri in aree relativamente piccole in tutta Italia.

Cosa rivela quindi questa analisi più dettagliata?

Sotto viene rappresentato il numero di persone decedute per tutte le cause nel Nord Italia (inclusa la Lombardia) a febbraio.

Il numero di decessi in ogni comune con una popolazione abbastanza grande da fornire dati significativi (di cui ci sono diverse centinaia nel Nord Italia) è stato codificato a colori in base al numero di decessi nel mese di febbraio rispetto alla media osservata nei cinque anni precedenti.

L'osservazione degna di nota è che, come ci si aspetterebbe in assenza di una pandemia, alcune aree mostrano decessi leggermente inferiori rispetto al "previsto" (azzurro chiaro o grigio), mentre altre leggermente superiori (verde o giallo). Tuttavia, il punto fondamentale è che non ci sono affatto cluster.

Vi è qualche area occasionale dove i decessi sono più che raddoppiati, ma va notato che alcune di queste aree sono piccole e di solito hanno solo uno o due decessi al mese, quindi un decesso o due in più rappresenterebbe un raddoppio.

Sia l'assenza complessiva di decessi in eccesso che l'assenza di cluster di decessi in eccesso sono fatti totalmente inaspettati alla luce delle prove — ora supportate da articoli che analizzano dati relativi agli anticorpi, ai test PCR e ai sintomi — della significativa presenza del SARS-CoV-2 a febbraio (e in realtà anche prima).

Questo grafico, riprodotto da The early phase of the COVID-19 epidemic in Lombardy, Italy, Careda et al., mostra il tasso di riproduzione stimato, derivato dai casi sintomatici nelle varie aree amministrative della Lombardia.

Come poteva un virus apparentemente altamente contagioso e letale essersi diffuso così tanto in tutta la regione, al punto che la crescita dei casi sembrava effettivamente rallentare nella maggior parte delle aree entro la fine di febbraio, senza lasciare alcun segnale di un aumento dei decessi dietro di sé?

È importante considerare che il Nord Italia rappresenta un "piccolo mondo" nel senso che tutte le città sono ben collegate tra loro. Tuttavia, le città stesse sono molto compatte. Quindi, la maggior parte dei contatti tra le persone avverrà all'interno della città in cui vivono, lavorano e socializzano. L'aspettativa — basata sull’emergere precedente di un virus patogeno capace di causare una mortalità in eccesso notevole — sarebbe stata che alcuni dei focolai iniziali avrebbero preso piede per primi, facendo scattare campanelli d’allarme in queste città. Come il fuoco nella foresta.

Vi sarebbe anche un'elevata probabilità di diffusione verso le città/comuni vicini — di nuovo, proprio come l'incendio nella foresta; va riconosciuto che la maggior parte dei contatti che potrebbero provocare trasmissione virale deriva da viaggi a breve distanza, mentre i viaggi a lunga distanza rappresentano una diffusione più limitata.

Tuttavia, a febbraio non c’è alcun pattern visibile che suggerisca un qualsiasi clustering, e un calcolo aritmetico del grado di auto-correlazione (che può essere fornito a qualsiasi parte interessata) lo conferma.

E per quanto riguarda marzo — il mese delle curve di decessi in eccesso scioccanti?

Ecco la mappa per marzo:

A prima vista, è visibile un clustering, quindi si potrebbe essere portati a pensare che questo rappresenti una prova della diffusione virale. Un'analisi statistica sull’auto-correlazione suggerisce anch’essa una diffusione.

Tuttavia, è importante capire che l’auto-correlazione può avere cause diverse dalla diffusione biologica. Ogni comune è situato all'interno di una specifica provincia; differenze tra queste province nella gestione della sanità e dell’assistenza sociale che hanno avuto un impatto sui tassi di mortalità potrebbero anch'esse determinare l’apparenza di clustering. Tuttavia, questo non è dovuto alla diffusione di un virus ma piuttosto alle similitudini tra comuni limitrofi nelle politiche sanitarie dovute al fatto di trovarsi nella stessa area amministrativa.

Adottando un modello con 2 componenti — in primo luogo la provincia amministrativa in cui il comune è situato, e in secondo luogo i tassi di mortalità nei comuni vicini — gli effetti della provincia e del comune limitrofo possono essere analizzati separatamente.

Ecco come appare la situazione di marzo quando l'effetto della provincia di appartenenza viene sottratto matematicamente:

Il clustering quasi scompare completamente quando si prendono in considerazione i confini provinciali (e l’analisi statistica lo conferma), il che significa che la provincia di appartenenza era un predittore di mortalità molto migliore rispetto alla presenza di un tasso di mortalità elevato nei comuni vicini.

Assumendo che il sistema sanitario sia organizzato per provincia, la conclusione può essere solo che il modo in cui l’assistenza sanitaria è stata erogata era rilevante per il tasso di mortalità, non la diffusione di un virus, che ovviamente non rispetterebbe i confini provinciali.

La discussione di cui sopra copre il periodo in cui i tassi di mortalità stavano salendo. Tuttavia, ci sono ulteriori prove a sostegno che possono essere ricavate dal modo in cui i tassi di mortalità in eccesso successivamente sono diminuiti.

Ricordiamo quanto suggerito sopra: mentre l’incendio nella foresta si estingue, i cluster si esauriscono progressivamente in tempi diversi, poiché si sono attivati in momenti diversi. Non ci si aspetterebbe che tutti gli incendi si estinguano contemporaneamente.

Tuttavia, gli incendi si sono tutti estinti più o meno contemporaneamente in Lombardia. A maggio si osserva un crollo sia dei decessi in eccesso che di qualsiasi pattern spaziale.

(Un’analisi relativa ad aprile — non mostrata qui — è simile a quella di marzo, in quanto qualsiasi auto-correlazione era debole e quasi completamente spiegata dalla collocazione provinciale.)

Vale la pena sottolineare, come ha fatto Michael Senger nel suo recente articolo, che i sostenitori della narrativa secondo cui “un nuovo virus mortale si è diffuso dal mercato del pesce di Wuhan a partire da dicembre 2019” si basano essi stessi sull’analisi spaziale e sul clustering dei casi intorno al mercato per sostenere la loro tesi.

Di seguito sono estratti dal loro articolo pubblicato nel luglio 2022.

Naturalmente, l’evidenza emergente di una diffusione precedente e più ampia demolisce imbarazzantemente questa tesi, così come solleva domande cruciali su ciò che è accaduto nel Nord Italia.

Conclusioni e osservazioni finali

L'analisi statistica dell'auto-correlazione dei decessi in eccesso in Lombardia nella primavera del 2020 rivela un clustering molto inferiore rispetto a quanto ci si sarebbe aspettato se un virus che si diffondeva nella regione fosse stato responsabile. Quel poco di clustering osservato sembra essere più correlato a differenze tra le regioni amministrative in cui sono situati i comuni.

Queste osservazioni sollevano certamente domande che devono essere approfondite sulle cause dei tassi elevati di decessi in eccesso nella regione lombarda nella primavera del 2020.

Il fatto che le assunzioni derivate dai dati della Lombardia — incluse le stime dei tassi di letalità e di mortalità per infezione — abbiano formato la base delle politiche implementate dapprima nel Regno Unito, e successivamente diffuse in tutto il mondo, rende questa una questione che dovrebbe essere affrontata con una certa urgenza.

Questo è particolarmente importante poiché queste politiche vengono ora riconosciute come causa di danni catastrofici e di lunga durata a gran parte della popolazione mondiale, con pochi o nessun beneficio osservabile.

Un sommario dello stato attuale delle prove sulla diffusione del SARS-CoV-2 nel 2019 è stato recentemente pubblicato nel BMJ da un gruppo di scienziati italiani, che include un'analisi sul perché l'argomento non ha ricevuto l'attenzione che merita. Questa analisi conteneva poche discussioni sulle implicazioni delle sue scoperte, concludendo comunque come segue (enfasi aggiunta):

Nonostante i limiti tecnici degli studi di origine disponibili, anche una remota possibilità che i test positivi indichino una circolazione precoce di SARS-CoV-2 dovrebbe essere considerata sufficiente per giustificare l'ampliamento della ricerca a più campioni, da più regioni, attraverso un arco temporale più ampio. Il tempo sta finendo: preziosi campioni che potrebbero contenere la chiave per comprendere l'origine del SARS-CoV-2 potrebbero essere già stati distrutti poiché i loro requisiti di tempo di conservazione normativa scadono. Molti altri subiranno lo stesso destino nei prossimi mesi e anni. Cosa c'è da perdere accettando questa ipotesi come plausibile ed esplorandola con urgenza prima che le possibilità di trovare le risposte per spiegare come questa pandemia è emersa scompaiano per sempre?

“Non perdiamoci in discorsi inutili! Facciamo qualcosa, mentre ne abbiamo ancora la possibilità… In questo luogo, in questo momento, tutto il genere umano siamo noi, che ci piaccia o no. Facciamo del nostro meglio prima che sia troppo tardi!” – Aspettando Godot, Samuel Beckett

Concordiamo pienamente, suggerendo al contempo che la ragione della riluttanza a discutere la questione della diffusione del virus nel 2019 si trovi probabilmente nella risposta alla domanda segnata in grassetto sopra.

Ciò che c'è da perdere — almeno per coloro che hanno spinto incessantemente una spiegazione narrativa unica per tutte le osservazioni dal 2020, censurando qualsiasi punto di vista alternativo con l'ossimoro “la scienza è conclusa” — è la realizzazione da parte dei cittadini che una larga proporzione di decessi potrebbe essere stata attribuita erroneamente a un virus piuttosto che alla loro vera causa: la natura della risposta percepita alla minaccia di un virus.

Siamo molto felici di condividere i file di dati originali, spiegare come sono stati trasformati dai dati scaricati dall'Autorità Statistica Italiana e descrivere la nostra metodologia nella creazione dei grafici sopra e nella misurazione dell'auto-correlazione. Invitiamo qualsiasi scienziato, soprattutto in Italia, a verificare la nostra analisi.

Un'area specifica per la quale saremmo grati di ulteriori informazioni è la natura e i tempi delle differenze tra le varie regioni amministrative della Lombardia nella fornitura di assistenza sanitaria e sociale. Presumiamo in quanto sopra che vi fossero differenze tra le regioni in questo senso, ma ulteriori dettagli sarebbero preziosi.

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Wood House 76

Sì, crediamo che la curva di mortalità per tutte le cause di Bergamo (Italia) sia una rappresentazione fraudolenta

Italian translation of Yes, We Believe the Bergamo (Italy) All-Cause Death Curve is Fraudulent (credit: Tommaso Zanini…

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a year ago · 19 likes · Jessica Hockett and Jonathan Engler

Keith’s background is in IT and business management, and he had a brief stint as a secondary schoolteacher. Keith’s writing has been published in The Critic and he has contributed to GB News in matters relating to transgenderism in schools.

In this talk, Keith explains why and how we must stop this indoctrination of our children as well as touching on Our Duty's place in the broader context, some of its achievements and ambitions.

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The project scientists have designed all the experiments to operate as legal vehicles for both private individuals and organisations to use in litigation against lockdown, masking and vaccination mandates, as well as in unfair dismissals cases in corporate settings and for those who have suffered vaccine damages.

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“I consider even the Natural Small-pox a mild disease, and only rendered malignant by mistakes in nursing, in diet, and in medicine, and by want of cleanliness… It would hardly be too bold to say, that the fatal treatment of this disease, for two centuries, by warming and confining the air of the Chamber, and by stimulating and heating cordials, was the cause of two-thirds of the mortality which ensued.”
― John Birch, Member of the Royal College of Surgeons, London, 1814, The Gentleman's Magazine, July 1814, p. 24.

Roman Bystrianyk is the Co-author of Dissolving Illusions: Disease, Vaccines, and The Forgotten History and Moving Back from Midnight: Working Together to Save Our Planet. He has been researching the history of diseases and vaccines since 1998. He has an extensive background in health and nutrition, as well as a B.S. in engineering and an M.S. in computer science.

Dissolving Illusions paints a historic portrait with quotes from the pages of long overlooked medical journals, books, newspapers, and other sources to reveal a startling history that has been disregarded. With this historic information and originally researched data in the form of myth-shattering graphs, Dissolving Illusions shines new light onto issues that are assumed to be clear-cut and settled long ago.

Roman’s work can be found at https://dissolvingillusions.com

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The Scottish COVID-19 Inquiry was set up in February 2022 to investigate the devolved strategic response to the WHO-declared “coronavirus pandemic” in Scotland, between 1 January 2020 and 31 December 2022. By utilising a themed approach to its investigations and hearings, its aim has been to establish the facts, identify lessons to be learned and make recommendations to Scottish Ministers, whilst working independently of government.

Established as a statutory public inquiry, the scope of the Inquiry’s investigation includes querying the impact on the provision of health and social care, education and certification, business, financial and welfare support.

Lord Brailsford, Chair of the Inquiry and a Judge of the Supreme Courts, has supervised the running of the Inquiry in an independent capacity, supported by the secretariat, legal team and Counsel. He will be responsible for delivering the final report to Scottish Ministers and has a statutory duty to act fairly, cannot make any findings of civil or criminal liability, nor be awarded any compensation.

The facts established so far

The Inquiry’s Health and Social Care Impact Hearings began on 24 October 2023 and concluded after 8 months on 28 June 2024. Public Health Scotland (PHS) described these hearings as an “opportunity to hear evidence from people across Scotland about the impact the pandemic had, and continues to have, on their lives”. However they in fact provided an opportunity to hear from family members, lawyers representing various groups, unions, care home managers and many chief executive officers and directors of organisations such as Scottish Care, Down Syndrome Scotland, Cerebral Palsy Scotland and Age Scotland of the abuse and neglect experienced by the people in the institutions that was unrelated to a pandemic.

Many of these testimonies proved to be extremely harrowing in nature and yet have been largely ignored by both the mainstream and alternative media.

The first seven months of the Inquiry is compiled below, spotlighting testimonies from lawyers and family members.

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Third Sector organisations, unions and care home managers told of shocking medical discrimination against young people with Down syndrome and paramedics ‘toe tagging’ patients over 50. Several care home managers acknowledged that COVID deaths recorded during lockdown 2020 included no testing at all and were likely due to other conditions.

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Closing statements of the Health and Social Care hearings

In spite of the first-hand evidence provided by the people of Scotland, to the best of my knowledge, none of the following testimonies have been covered by the world’s media.

Through the Scottish Covid Enquiry, the following has been confirmed:

1. Severe lockdown isolation harms, particularly for care home residents with dementia who suffered the highest death toll from COVID-19

2. Neglect, dehydration and starvation of care home residents

3. Suspension of basic human rights (Articles 2, 3 and 8 of the European Convention engaged)

4. No GPs or ambulances were available for sick care home residents during lockdown

5. Misuse of (Do Not Attempt CPR) DNACPR orders for the elderly, those with Down syndrome and even those suffering hearing loss

6. Overuse of end of life medications in care homes

7. Preventable excess deaths during lockdown.

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If you want to know more

Most of these testimonies can be seen in greater detail, individually on my Substack. There are other submissions not covered here, for example Dr. Gail Cunningham’s evidence of COVID vaccine harms, and Union member Susanne Napier who also talks about the serious harms witnessed from mask wearing. The ‘‘horrendous’’ harms of lockdown on children is also made clear by lawyer, Rachel Holt.

My hope is people more qualified than myself can elevate the discussion as to the true nature of the ”COVID pandemic”. The evidence from the world’s only official COVID-19 inquiry revealing what really happened during lockdown has been laid bare.

The conclusion of Denis Rancourt’s new paper is where the weight of quality real-world evidence now lies.

“We are compelled to state that the public health establishment and its agents fundamentally caused all the excess mortality in the Covid period, via assaults on populations, harmful medical interventions and COVID-19 vaccine rollouts. We conclude that nothing special would have occurred in terms of mortality had a pandemic not been declared and had the declaration not been acted upon.”

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PANDA focuses on open inquiry, sense-making of scientific developments and the drivers influencing our societies, with an emphasis on the events that mark the Covid era. We invite submissions of articles from experts, enthusiasts, and professionals willing to share their expertise. Please submit your articles to our editorial team at media@pandata.org.

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A first problem with the tweet is that it is a word-for-word rehash of a year-old tweet from the X-user who goes by the name “Pelham”. (Hat-tip: @org_scp). The original tweet can be seen below.

Just why Robert Malone would have chosen to repeat the content of the earlier tweet word-for-word is unclear. This appears to be bot-like behaviour. Furthermore, “Pelham” already had posted (not reposted) the same tweet several times over the last year, most recently on July 17^th, which is presumably when Robert Malone saw it. See here, here, and here. This too is bot-like behaviour. Pelham’s tweets have also invariably gone viral, raking in hundreds of thousands and sometimes millions of views.

But the second even more serious problem with the tweet is that its core claim is patently false. Orgenesis is not owned by Pfizer. Orgenesis is a relatively closely held company, nearly 40% of whose shares are owned by just two people: Jacob Safier and Yehuda Nir. See the below chart from the Orgenesis annual report here (p. 75).

45% of the stock is held by the two principal owners plus officers of the company. Lists of institutional investors (including mutual funds) can be consulted here and here. Pfizer is nowhere to be found.

The claim that Pfizer owns Orgenesis appears to be a pure fabrication. And yet this pure fabrication has been trending wildly on X.

This is the third problem with the tweet. Elon Musk’s obligations toward the EU under the Digital Services Act – whose “goals of transparency, accountability and accuracy of information are aligned with ours”, Musk has said – include a commitment to combatting misinformation. Misinformation “safety labels” are built right into the code of the X algorithm, as can be seen below. Accounts or posts that get these labels slapped on them are having their visibility suppressed even if the account-owners are unaware. It is literally a system of “censoring without telling anyone”, to paraphrase a recent Musk remark.

Furthermore, Musk has repeatedly insisted – including in the above-cited tweet – that Community Notes would be “transformational” in achieving the goal of “accuracy of information”. Thus, posts which manage to achieve visibility or even start to go viral – perhaps because the account owner did not previously have a “safety label” on his or her account – will frequently be targeted by a “Community Note”, which essentially contests the veracity of the post right in the post!

But the Malone tweet and the prior “Pelham” tweets have trended unabated on X without a peep from “Community Notes” despite the fact that the core claim is false. The “Pelham” tweets have garnered over 3 million views and the Malone tweet, as of this writing, has surpassed 300K.

So, why does this patent misinformation trend?

I would submit that it is because the X algorithm in fact serves to do the exact opposite of what is claimed. It serves to amplify misinformation and suppress information.

That the X algorithm would work like this was in fact inevitable from the moment a political authority was made the arbiter of information and misinformation, of truth and falsity. The Digital Services Act (see here, here and here) makes the European Commission this arbiter, hence it is inevitable that the X algorithm, so long as X wants to remain on the EU market, will promote misinformation that the European Commission find useful and suppress information that is inconvenient for it.

Simply put, the Heiko von der Leyen/Orgenesis/Pfizer story is a red herring. It does not hurt Ursula von der Leyen. It helps her, precisely because it is bogus and by diverting attention from some inconvenient and verifiable facts.

These facts are the following:

1. The vaccine contract to which reference is made in the Malone/“Pelham” tweet was not a contract with Pfizer but rather with a consortium consisting of Pfizer and the German company BioNTech. BioNTech, moreover, is the marketing authorisation holder for the product in the EU, its legal manufacturer and has been the main financial beneficiary of its sales. (The value of the contract, by the way, was also not €71 billion. That was the value of contracts signed with all Covid-19 vaccine manufacturers.)

2. The German government (as documented here) has been the state sponsor of BioNTech during the company’s entire existence. Indeed, the German government sponsored the very founding of the company in 2008, as part of a “Go-Bio” programme whose objective was to make Germany into a world leader in biotechnologies.

3. Ursula von der Leyen was a member of the German government that sponsored the founding of BioNTech in 2008 and was a member of each of the three successive German governments that continued to subsidise the company during the lean years prior to Covid and into the Covid period. After she had resigned her position as Minister of Defence to become President of the European Commission, the last of the German governments in which von der Leyen served would go on to provide nearly €400 million in subsidies to BioNTech’s Covid-19 vaccine project. Under von der Leyen’s leadership, the EU itself provided another €150 million in debt financing to the company via the European Investment Bank. (For documentation, see here.)

This is the conflict of interest that has been at the very heart of the EU’s Covid-19 vaccine procurement process from the start. It has nothing to do with any personal conflict of interest of Ursula von der Leyen. It is an institutional conflict of interest involving the German government, a German government official who was parachuted into the position of European Commission president – i.e. Ursula von der Leyen – and a state-sponsored German company, BioNTech, which was the main commercial beneficiary of the EU’s programme of mass Covid-19 vaccination and the EU contracts.

Your donation supports independent insights. Donate now.

Roman Bystrianyk is the Co-author of Dissolving Illusions: Disease, Vaccines, and The Forgotten History and Moving Back from Midnight: Working Together to Save Our Planet. He has been researching the history of diseases and vaccines since 1998. He has an extensive background in health and nutrition, as well as a B.S. in engineering and an M.S. in computer science.

Dissolving Illusions paints a historic portrait with quotes from the pages of long overlooked medical journals, books, newspapers, and other sources to reveal a startling history that has been disregarded. With this historic information and originally researched data in the form of myth-shattering graphs, Dissolving Illusions shines new light onto issues that are assumed to be clear-cut and settled long ago.

Roman’s work can be found at https://dissolvingillusions.com

Your donation supports our independent insights. Donate now.

The passage in question was highlighted by Aya Velazquez, the journalist to whom the documents were provided, in the July 23^rd press conference that she held with two other German Covid-response critics.

It reads as follows:

EMA and Pfizer are considering whether they might skip the phase III trials and go directly to broad use, if the regulators decide to do this, then it [the authorization process] could go quicker than 12-18 months.

The prior bullet-point in the minutes affirms that “normally” the authorization process of a new vaccine is expected to take “12-18 months from the start of phase I”.

The passage clearly alludes to the authorization process of what has come to be commonly known as the “Pfizer” Covid-19 vaccine, even though the actual developer and legal manufacturer is the German company BioNTech. The EMA is the European Medicines Agency.

But the problem with the passage is, above all, the date of the minutes in which it is included: April 15^th, 2020. The passage is wildly anachronistic. There is no reason why Pfizer should have had any contact with the EMA on authorization of the BioNTech-Pfizer vaccine so early.

Indeed, there is no reason why Pfizer would have had any official, direct contact even later. The applicant for the authorization was not Pfizer. It was BioNTech. This is also the case for the application for full authorization in the USA. But in the US, BioNTech, as a foreign firm, had to appoint a domestic agent, and it appointed Pfizer to play this role. The company had no need for any intermediary in the EU.

On April 15^th, 2020, neither company would have yet had the occasion to discuss their long-term plans with the EMA. BioNTech had registered its Phase I trial of its Covid-19 vaccine candidate with the EMA literally the day before! See the entry from the EMA’s Clinical Trials Register below.

This registration will have been BioNTech’s first official contact with the EMA in the matter of its Covid-19 vaccine candidate. As indicated in the record, the trial was to be conducted under the aegis of the German regulatory agency, the Paul Ehrlich Institute (PEI).

At this stage, the PEI was undoubtedly BioNTech’s principal interlocutor. According to BioNTech CEO Ugur Sahin, he and his team first met with the PEI to discuss the company’s Covid-19 vaccine project more than two months earlier, on February 6^th. (See The Vaccine, p. 52.)

It is important to note that while BioNTech and Pfizer had concluded their Covid-19 vaccine collaboration agreement in mid-March, Pfizer was not involved in the German Phase I trial, which was registered with the EMA on April 14^th and which began with the first vaccination of a human subject in Mannheim on April 23^rd.

We do know that Sahin was in fact extremely concerned about accelerating the pace of the authorization process. It is hard to imagine that in mid-April 2020 he was thinking so far ahead to the Phase III trials. Having not yet initiated the “first in human” Phase I trial, how could he even know he would get there?

But normal regulatory steps had to be skipped or somehow abridged in order for BioNTech even to initiate its Phase I human trial in April. In particular, BioNTech had to be allowed to initiate the human trial without first completing a preclinical toxicology study on animals.

As discussed in detail here, the PEI in fact allowed BioNTech to do this, permitting the firm to start its human trial on the basis merely of an “interim” toxicology report before the final results were available. On Sahin’s account, BioNTech received the go-ahead from the PEI on April 21^st. (See The Vaccine, p. 173.)

It makes little sense for the RKI crisis team to have been discussing Pfizer and the EMA in the April 15^th minutes. It would have made a lot of sense for them to be discussing BioNTech and the PEI. The RKI was surely being kept informed by its sister agency about the furious development of BioNTech’s vaccine project (the two agencies bear roughly the same relationship as the CDC and the FDA in the United States).

If not, Germany’s then-Minister of Health Jens Spahn could have told RKI staff about it. In his April 1^st, 2021 speech inaugurating BioNTech’s mRNA manufacturing facility in Marburg, Spahn said that he first met BioNTech CEO Sahin to discuss the company’s Covid-19 vaccine project “around 12 months ago”. This would take us to late March/early April.

The PEI is mentioned twice just a little further on in the same April 15^th minutes, and the April 2^nd minutes report that the PEI informed the RKI that a “clinical study will receive the authorization tomorrow”. Curiously, the extant minutes do not tell us whose clinical study.

Other than in a November 11^th quote from the British magazine Nature, Pfizer is never again mentioned in the 1834 pages of the 2020 minutes, independently of BioNTech. All further references are to “BioNTech-Pfizer”, “BioNTech/Pfizer” and the like. BioNTech is often mentioned alone.

The complete RKI files in the versions provided to Aya Velazquez are available here. German observers quickly spotted that the leaked versions are not in fact identical to the official versions that had been previously released in redacted form to the journalist Paul Schreyer and then published, in already somewhat less redacted form, on the RKI website. (See here, for instance, for one comparison.) Whole passages from the previous release are in fact missing from the leaked version (and at least one of those passages, incidentally, contains redactions) and the language is not the same in others.

Aya Velazquez explained the discrepancies by noting that she herself had received multiple versions of the documents from her source as Word docs and had selected those “closest” to the versions on the RKI website to include in the originally posted PDFs. She has, in the meanwhile, also posted these Word documents.

But, as critics have noted, a Word document can be altered at will. Some of the critics claim that metadata shows modifications being made – presumably by the Robert Koch Institute itself – well into 2024. What is the point of keeping minutes if they are going to be altered ex post? If the RKI created multiple versions of the crisis group minutes, what does it even mean to speak of an authentic version anymore?

Original version published on Daily Sceptic

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David Charalambous shows how beliefs are installed and then defended. He will demonstrates how false beliefs were created during the "pandemic", and how those beliefs formed an unconscious web that then drove behaviour and thinking.

David is a behaviour and communication expert and founder of ReachingPeople.net, a project dedicated to helping people communicate with those of differing views and showing people how institutions and corporations manipulate us. He has spent the last four years assisting many groups in understanding how governments and institutions have used behavioural science knowledge to influence people. David believes we can improve our effectiveness by understanding certain principles of behaviour and communication.

https://reachingpeople.net

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The “story of the decade”, “smoking gun”, “case closed”. The story of US-funded – or, as the case may be, even not funded – gain of function research on coronaviruses has been widely presented as the solution to the riddle of Covid: not only the nearly definitive proof that SARS-CoV-2 was created in a laboratory, after all, but also the nearly definitive demonstration of whodunnit.

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The Americans did it, of course. Or “we did”, as Jim Haslam, the American substacker who developed the most elaborate version of this theory, has put it: Anthony Fauci, the funder of the research; Ralph Baric, the “designer” of the virus; and Peter Daszak, the albeit British head of US-based EcoHealth Alliance, who directed the research. They merely needed a little help from one hired-gun Dutch virologist in the person of Vincent Munster, who made Baric’s virus transmissible at Fauci’s Rocky Mountain Lab in Montana before it was shipped to Wuhan. And the rest is history.

But what about all the German connections to virus research in Wuhan which I have documented here, here, here and here and which involve not just German funding for virus research in Wuhan, but indeed a full-fledged German-Chinese virology lab in Wuhan, which – unlike the Wuhan Institute of Virology – is located right in the area of the initial outbreak of Covid-19 in the city.

It is all the more remarkable that these German connections are being ignored given that the supposedly “American” story of the creation and release of Covid-19 points right back to them: namely, to a German or, more exactly, German-Dutch coronavirus research nexus, which has played a key role in the Covid-19 response and at whose centre we find none other than Christian Drosten. Drosten is, of course, the German creator of the notoriously hypersensitive and unreliable Covid-19 PCR test which was the very basis of the declaration of a pandemic.

Let us start with what is invariably described as Anthony Fauci’s Rocky Mountain Lab, where Vincent Muster is supposed to have made Baric’s virus design transmissible. The headline of a Daily Mail article even describes it as a “Fauci-run” lab. Well, although indeed a NIAID research facility and to that extent connected to the former NIAID director Fauci, the actual director of the facility itself is the German virologist Heinz Feldmann.

So what? – you might say. There are 80 million Germans. True enough. But there are not 80 million Germans who have conducted virus research with Christian Drosten.

Indeed, in addition to Feldmann and Drosten, there are two other notable authors of the above-cited 2011 Ebola virus paper: Vincent Munster, Feldmann’s employee at the Rocky Mountain Lab who needs no further introduction here, and Stefan Pöhlmann, a virologist based at the German Primate Centre in Göttingen. Like Drosten, Pöhlmann took part in the famous February 1^st 2020 conference call with Anthony Fauci on a possible lab-leak of SARS-CoV-2. As will be seen momentarily, it was precisely what might be called the “EU crew” around Drosten, and including Pöhlmann, which would attempt to beat back the lab-leak hypothesis in the discussions with their Anglosphere counterparts, both on the call and in e-mails which followed.

The German physician and virologist Johanna Deinert was one of the first to point to the many German links to the virus research which may have contributed to a laboratory origin of SARS-CoV-2. Her @DeinertDoc Twitter account was suspended under the old regime and has never been restored under the new. The pseudonymous Seven of Nine, MD X account has taken up many of the same themes.

In an X thread, Seven of Nine, MD writes that Feldmann “owns the SARS1 genome with Frank Plummer via patent, created bat cell lines in 2011. In SARS-CoV-2 we find inserts leading to Plummer (2015/2017) and B. Korber (2011)…”. The referenced patent can be consulted here. Citing the above paper and referring to a research project at the Rocky Mountain Lab involving fruit bats and a “SARS-like” virus, Seven of Nine, MD adds, “The fruitbat cell cultures were developed in collaboration with none other than Christian Drosten and Stefan Pöhlmann”.

It should be recalled from the FOIA’d “Fauci e-mails” that after the spectre of a laboratory origin of SARS-CoV-2 was first raised with Fauci by Kristian Andersen, Jeremy Farrar of the Wellcome Trust would arrange for the famous February 1^st conference call, bringing in a German/Dutch team of coronavirus experts to discuss the matter with their dismayed Anglosphere colleagues. The concerned Anglosphere scientists were Robert Garry, Andrew Rambaut, Edward Holmes and the Danish virologist Andersen, who is, however, based at Scripps Research in California.  All of them suspected that the virus had a lab origin or were even convinced that it did. Even Farrar, who has been dismissive about lab-leak in his public statements, said he was “50-50” between lab-leak and natural origin behind the scenes.

But it was members precisely of the German-Dutch “EU crew” who are reported to have lambasted Andersen and his Anglosphere colleagues on the conference call and who would continue to urge, in subsequent correspondence, that the matter be, in Drosten’s words, “dropped”.

We have already noted Heinz Feldmann’s connections to the two German members of the EU crew, Christian Drosten and Stefan Pöhlmann. The other two members were the Dutch virologists Marion Koopmans and Ron Fouchier.

Koopmans is head of the Erasmus University Medical Centre’s department of Viroscience and co-author of Drosten’s controversial PCR-protocol paper. Following a lightning-fast 24-hour “peer review”, the paper had been published by the EU-funded journal Eurosurveillance just one week before the conference call. Fouchier is none other than Europe’s and perhaps the world’s most famous or infamous, depending on one’s perspective, gain-of-function researcher. He is Koopman’s deputy at the department of Viroscience of the Erasmus Medical Centre Rotterdam. He is also co-author of the 2003 SARS-CoV–1 paper which, in the words of Seven of Nine, MD, “launched Drosten’s career”.

So, what does that have to do with Vincent Munster’s alleged concoction of SAR-CoV-2 at Feldmann’s (not Fauci’s) Rocky Mountain Lab? Well, Munster is Fouchier’s student! Fouchier was the co-director of Munster’s PhD thesis (as can be seen here), along with Ab Osterhaus, who is perhaps the key historical figure in the formation of the German-Dutch virology nexus. Osterhaus was the head of the Viroscience department at the Erasmus Medical Centre Rotterdam until 2014, when he was succeeded by Koopmans. The 75-year-old Dutchman presently leads a “One Health” working group at the University of Veterinary Medicine in Hannover.

(In a coy February 9^th Slack message, after noting the possibility of an accidental release of the virus, Robert Garry wrote: “Call me conspiratorial…, but I think there may be some hallway talk going on at Erasmus.”)

Neither Koopmans nor Pöhlmann appear to have played an active role in the conference call. As the FOIA’d e-mails and the related Slack messages make clear, “Christian” and “Ron” led the charge. Along with Koopmans, they would keep the pressure on in the subsequent e-mail exchanges, ultimately getting Andersen and his Anglosphere colleagues to recant their original theory and to endorse precisely the opposite theory, i.e. that of a zoonotic origin of SARS-CoV-2, in their now infamous “Proximal Origin” paper. (See, for instance, the anonymous “whistleblower” e-mail to Jon Cohen of Science magazine reproduced here. The two “world-class” coronavirus experts to whom reference is made are undoubtedly Drosten and Fouchier.)

Now, if you were a detective investigating a crime – for instance, the creation of a supposedly deadly virus (whether it was in fact so deadly is, of course, another matter) – whose behaviour would you find suspicious? The behaviour of those who themselves expressed concern about a lab-leak and were keen on investigating the matter – including, n.b., none other than Anthony Fauci, who even suggested contacting the FBI – or the behaviour of those who were dismissive and defensive and tried to shut the conversation down?

“Didn’t we congregate to challenge a certain theory and, if we could, drop it?” Christian Drosten asked with notable irritation in a terse February 9^th 2020 e-mail to the other members of the group, “…Who came up with this story in the beginning? Are we working on debunking our own conspiracy theory?”

Do these sound like the words of an innocent man? Not much, especially if we consider that before this e-mail emerged thanks to an American FOIA request, Drosten had insisted in a sworn statement to a German court that he “had no interest in steering the suspicion about the origin of the SARS-CoV-2 virus in a certain direction. In particular, I had and I have no personal interest in ruling out the so-called laboratory thesis….” No interest in steering the suspicion in a certain direction?! How is that consistent with “didn’t we congregate to challenge a certain theory, and if we could, drop it”?

Drosten, as discussed in my “Why Fauci, not Drosten?”, has links to the German-Chinese virology lab in Wuhan and its German co-director Ulf Dittmer. As seen in the below photo, a 2015 German-government-sponsored virology symposium in Berlin brought together not only Drosten and the bat coronavirus specialist Shi Zhengli of the Wuhan Institute of Virology, but also both the German and the Chinese co-directors of the German-Chinese lab, Dittmer and Dongliang Yang, and both the then and apparently even the current directors of the WIV to boot! The director at the time, Chen Xinwen, is the small, buck-toothed man with the blue tie in the photo. The young woman with the long black hair in the lower left-hand corner appears to be current WIV director Wang Yanyi, although Wang is not listed as a participant in the event programme.

In January 2020, moreover, Drosten told the Germany daily Die Berliner Zeitung that he had learned about the supposedly novel virus in Wuhan from virologist colleagues in the city before any infections had even been officially reported!

If a virus engineered in Montana is supposed to have somehow gotten to the Wuhan Institute of Virology, even though the WIV was not even a part of the US-funded CREID project which is alleged to have made the connection, why could it not have gotten to the German-Chinese virology laboratory on the other side of the Yangtze? Moreover, the CREID project – which is in fact more a network than a project – was not even launched until 2020: several months after the official start of the Covid-19 outbreak in Wuhan.

As the below map from Science magazine makes clear, the campuses of the Wuhan Institute of Virology are in fact nowhere near the area of the initial cluster of Covid-19 cases in Wuhan. The German-Chinese lab, by contrast, is right in the cluster. It is located at Union Hospital, designated by number 6 in the map. The Chinese co-sponsor of the lab, Tongji Medical College, is located virtually at the very epicentre of the outbreak: roughly one kilometre to the north of Tongji Hospital, which is designated by number 5 in the map.

Moreover, leaving aside whatever research was being done in Montana or, say, Chapel Hill, what do we know about the research which was being conducted at the German-Chinese virology lab in Wuhan itself? Not much.

In September 2021, one Reinhard G. used a website dedicated to posing questions to German parliamentarians in order to ask the German member of the European parliament Christian Ehler whether gain-of-function research was being conducted at the German-Chinese lab. Ehler is chair of the EU parliament’s Panel on the Future of Science and Technology (STOA). Reinhard G. did not receive an answer. Ehler’s team merely noted that they did not know.

But why are no German journalists or commentators, many of whom have shown great interest in DEFUSE and CREID, demanding to know? The DEFUSE proposal was not funded. The CREID network is funded by the US government, but the Wuhan Institute of Virology is not part of it and it only got underway well after the initial outbreak of Covid-19 in Wuhan. The German-Chinese lab was launched in 2017 and it is funded by the German government. It grew out of a German-Chinese “transregional research centre”, TRR60, which was publicly-funded for a full decade from 2009 to 2018 and which, as I have shown here, proudly featured the Wuhan Institute of Virology as a partner. Indeed, though not the official co-sponsor, the WIV, as I have shown here, was in fact the main Chinese partner in the German-Chinese joint project in terms of personnel. One key sub-project, as will be touched upon momentarily, was being conducted at the WIV.

Why should the world not know as much about the German-Chinese lab and the German-Chinese “transregional” partnership as it knows about DEFUSE and CREID? Where are the Freedom of Information requests? Why is the German government being spared them? If anyone asked, at least it could say no, which would be revealing in its own right.

We know that the German government funds gain-of-function experiments, because none other than Christian Drosten was coordinator of a multi-part publicly-funded RAPID project which includes them, as can be seen below. Stefan Pöhlmann was, incidentally, director of one of the RAPID sub-projects.

Of course, if we only have US information, we will end up telling a US story. But if a foreign power really did have a hand in the escape or release of an engineered virus in Wuhan, then prima facie Germany is the far likelier suspect. All the more so, since some of the German-funded research being conducted at none other than the WIV under the aegis of the German-Chinese partnership involved precisely the so-called “HIV inserts” in SARS-CoV-2 which first gave rise to suspicions that the virus was genetically engineered. For that story, see my article “The Smoking Gun in Wuhan”. Perhaps, after all, the virus did not have to get all the way from Montana, but merely to cross the Yangtze…

An earlier version of this article was published on The Daily Sceptic

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Randall Bock MD is a primary care physician practicing in the Boston Massachusetts area. He is a published author, having written the book ‘Overturning Zika’, which brings to light the highly speculative, highly doubtful underpinnings of that pandemic.

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The RCP’s “green physician toolkit” is precisely the same patronising nonsense as the WHO’s toolkit of the same name, discussed in my Daily Sceptic article a few months ago.

The two main ideas behind the toolkit are
a) that doctors routinely make “unnecessary” decisions that have environmental impact, and that
b) they should use their authority to advance an ideological agenda.

For example “reduce blood testing where clinically appropriate”. This assumes that doctors test people’s blood frivolously, something equivalent to an “unnecessary journey”. Very, very odd.

But the most egregious claim, of course, is that “Climate change is one of the biggest threats to human health”.

That is so far from truthful, and so utterly embedded in green ideology, that the word “lie” is not sufficient.

There is less than zero evidence that climate is a “threat to human health”, let alone the “biggest threat”. The evidence debunking the link between climate change and health is so strong that doctors have to go out of their way to conceal the stats.

You can see the raw data here for yourself. Climate-related mortality has fallen in recent decades. Diseases of poverty, which are claimed to be climate-linked, including malaria, are also in steep decline.

I give an overview of how climate-activist doctors at the Lancet are influenced by ideology here, and go into more detail to explain how they lie in this presentation below. At around 36 mins, I show that the Lancet’s green propaganda pages hide the mortality statistics behind “frequency of event” statistics, to give the opposite impression.

The Lancet claims that “life-threatening extreme weather events are becoming increasingly frequent”. When you interrogate the data, however, it reveals that far fewer people are being killed by extreme weather.

So it’s not just a lie, it’s a fraud. And it’s a fraud told for political effect, to transform society and the economy, which ultimately will have a devastating effect on human health. And it already is, if we calculate the opportunity cost for people in emerging economies.

Doctors and medical researchers involved in this propaganda have invented their own metric of “risk” to create “evidence” of a growing “threat”. And doctors and institutions of medicine that reproduce this propaganda do so *uncritically* without checking the numbers.

The numbers are not complicated. The science is not complicated. The data is not hard to find. This is not like 5-sigma high energy physics experimentation.

Consider this chart from the latest Lancet propaganda report, for example. It claims that the better part of a $trillion is lost from agricultural production because of exposure to heat. But look at how small that effect is in the West, compared to developing/low income economies.

Whereas farmers in the US can tend to vast farms, sitting in GPS-guided, air conditioned, diesel-powered tractors, subsistence agriculture is dawn-to-dusk manual labour.

The difference is access to fossil fuels. It’s not a weather or climate effect.

To frame that disparity between agricultural producers in the richest and poorest economies as one of “climate change”, requiring the abolition of fossil fuels is a grotesque injustice. It is vile.

Ben Pile (https://x.com/clim8resistance) is an independent writer and researcher, filmmaker and commentator. He has been writing on the science and politics of climate and the environment for publications since the mid-2000s and is a frequent contributor to shows on GB News, Talk TV and others. He is the co-founder of Climate Debate UK, which helps the public, journalists and other organisations find the information necessary for democratic discussion about climate and energy policy. His work can be found at the following sites: http://benpile.substack.com; UKclimatedebate.co.uk

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"It's horrifying how many in the corporate sector support our transition to stakeholder capitalism."

Richard Jeffs is a former ITV producer and director-of-photography. He is now an independent investigative filmmaker and a tech start-up CEO, with an interest in the future of work.

In 2020, Richard produced "The New Normal" documentary, which was the first viral film to investigate the World Economic Forum and The Great Reset.

Over the past sixteen months, Richard has been devoted to making a powerful new series of feature films which investigate Stakeholder Capitalism. Enacted in 2020 by the World Economic Forum’s Stakeholders, Stakeholder Capitalism is a new political system currently being mandated by governments, aligning with Klaus Schwab's intent to transition the world to full Stakeholder Capitalism by 2030.

The first of these feature films is “Stakeholder Communism”, available to view now.

See more: https://yellow.forum

Last month, the biotech division of the German firm Wacker inaugurated a spanking new mRNA production facility in Halle, where, per the company, il will be able to manufacture up to 200 million doses of mRNA vaccines per year. The facility reportedly cost some €100 million to build.

But 200 million doses of mRNA vaccines for what?

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The only mRNA vaccines to be marketed thus far are, of course, the COVID-19 mRNA vaccines; innumerable safety issues have been and continue to be raised about those products; even some of their most fervent promoters have, in the meanwhile, raised questions about their performance, and it is now nearly universally, if often just tacitly, acknowledged that they do not do what they were authorized to do – namely, prevent COVID-19; and, in any case, the COVID-19 pandemic, if it ever truly was, has been officially over for two years now.

So, why did Wacker invest €100 million in a factory to produce mRNA vaccines for a non-existent market and how could it even afford to do so?

The Minister of the Economy of the German state of Saxony-Anhalt, Sven Schulze, was on hand for the inauguration ceremony, and even he appeared to be somewhat perplexed about what he was doing there. “It’s actually a little bizarre,” he told Germany’s ARD public television with a sheepish smile, “We hope that this sort of thing doesn’t happen again, the COVID pandemic is behind us….”

But the solution to the riddle is to be found on the federal level: in German government policy and largesse. For even if the public’s taste for mRNA drugs has waned, to say the least, the German government’s devotion to them has never foundered. This is hardly surprising since, as touched upon here, here and here, the German government has made mRNA manufacturing into a central plank of its industrial policy in the hope of turning the country into a global “hub” for mRNA vaccines.

Thus, already in March 2022, the German government announced that it had concluded so-called “pandemic preparedness contracts” with no less than five manufacturers or consortiums to reserve vaccine manufacturing capacity for future use. The recipients of the contracts included Germany’s leading mRNA companies BioNTech and CureVac (in partnership with GSK), as well as Wacker, Celonic and IDT Biologika.

As the ÄrzteZeitung, a specialized German newspaper for MDs, noted at the time, “of the suppliers, only BioNTech … has a product on the market to date.” BioNTech is, of course, the developer and legal manufacturer of what is more commonly known outside of Germany as the “Pfizer” COVID-19 vaccine. CureVac also had a COVID-19 vaccine candidate, but it did not gain approval.

Total government payments to the companies from 2022 through 2029 are supposed to total nearly €3 billion.

The capacity was ostensibly being reserved for domestic German usage. But given the sheer size of the capacity being reserved, with each of the companies or consortiums committing to production of up to 80 million doses annually – and BioNTech saying it would produce at least that much –  this justification never made much sense. Why would a country of just 80 million people need 400 million or more vaccine doses per year?

Note, furthermore, that as mentioned above, Wacker alone has in fact created capacity to produce up to 200 million doses under the aegis of the program. So, if the other participants have done or are doing likewise, we will in fact be seeing capacity for up to one billion doses per year being created.

The new capacity, moreover, is in addition to the capacity of BioNTech’s already existing mRNA manufacturing facility in Marburg, which the company purchased in 2020 in anticipation of the approval and rollout of its COVID-19 vaccine. That facility, per the company’s own figures, would give BioNTech alone the capacity to manufacture some 750 million doses annually. So, total German capacity will presumably reach over 1.5 billion doses per year – obviously far more than just the German domestic market could ever absorb. In the spirit of the vaccine “hub” project, the more plausible rationale for the program and the de facto subsidies is thus to create and maintain German capacity to supply the European and global markets. This is indeed obliquely admitted in the original German government press release, which notes that the contracts are “internationally expandable” and will thus “also make an important contribution to EU-wide and global vaccine supply in the medium- and long-term.”

From an article published by Daily Sceptic

The recent publication of thousands of pages of minutes of the COVID-19 “crisis team” of Germany’s Robert Koch Institute (RKI) has caused a buzz in social and even some traditional media, since they appear to show that the German government decided on many of the most draconian lockdown and containment measures for which the country became known against the scientific advice of its own public health authority! Germans were, for instance, obliged to wear not just any old masks but nothing less than high-filtration FFP2 masks, even though the minutes contain numerous warnings that wearing FFP2 masks is only appropriate for qualified medical personnel for short periods of time and that sustained use by the general public could even be dangerous.

But if Germany went into a hard lockdown – including schools closures, shop closures and a ban on public gatherings – without any scientific justification, the obvious question is: Why? Paul Schreyer is the co-editor of the German online magazine Multipolar, and it was his freedom of information request which would lead the RKI ultimately to release the documents, albeit in heavily redacted form.

It should be noted that the RKI turned over the documents to Multipolar of its own accord. Contrary to what has been widely reported, it was not ordered by any court to do so, although Multipolar speculates that it took the “surprising” decision in order to avoid a court order. It should also be noted that on the magazine’s own account, the RKI turned over the documents not two weeks ago, but in April 2023. So, Multipolar had the documents for nearly a full year before making them publicly available.

Schreyer suggests that even the RKI’s March 2020 decision to “upscale” its assessment of the risk represented by COVID-19 from “moderate” to “high” was made under political pressure and had no scientific justification. That decision was announced by then RKI president Lothar Wieler on March 17^th and would, as Schreyer says, serve as the basis for all the subsequent lockdown measures. Angela Merkel would announce radical nationwide measures on March 22^nd. But, Schreyer argues, if the number of Covid “cases” did indeed triple in Germany in March 2020, this was only because the number of Covid tests being administered tripled as well!

If there was political pressure, however, where did it come from? Schreyer alludes to external sources. The usual suspects, as it were: Bill Gates, what he calls the “US pandemic-management scene” and the WHO.

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The suspected vector of US influence is one Heiko Rottmann-Großner: a German Ministry of Health official “with excellent contacts to the US pandemic-management scene”, as Schreyer puts it. His only evidence of these “excellent contacts” is Rottmann-Großner’s participation in a one-day pandemic preparedness exercise which was held in Munich in February 2019 under the auspices of an American NGO. But given that the exercise was held in conjunction with that year’s Munich Security Conference, the annual transatlantic security pow-wow which is sponsored by the German government, it would have been extremely surprising if a representative of the latter had not been invited.

Indeed, none other than the Robert Koch Institute even plays a starring role in the fictional scenario on which the exercise was based, as can be seen here. It is the RKI which discovers that the plague strain (sic.) which is spreading across the world and has shut down global travel was…wait for it…genetically engineered! This must have been flattering for then RKI president Lothar Wieler, who, though not mentioned in the programme, was also present at the exercise, as pictorial evidence uncovered by Schreyer shows.

The Bill and Melinda Gates Foundation has been one of many, both private and public, supporters of NTI, the NGO which hosted the exercise at the Munich Security Conference. For what it is worth, the Canadian Ministry of Foreign Affairs (Global Affairs Canada) funded the exercise.

But, in any case, Heiko Rottmann-Großner is precisely a German government official – and one of longstanding to boot. As Schreyer notes, he was already the chief of staff of then German Minister of Health Hermann Gröhe in Angela Merkel’s 3rd government from 2013 to 2018.

It was Gröhe’s ministry, incidentally, which sponsored the symposium which brought together Germany’s “star virologist” Christian Drosten and the Wuhan Institute of Virology’s star bat coronavirus researcher Shi Zhengli – as well as many other luminaries of the German and Chinese virology scenes – in Berlin in 2015. (See my articles here and here.)

When Gröhe was replaced as German Health Minister by Jens Spahn in Merkel’s 4th government, starting in 2018, Rottmann-Großner would stay on in the ministry as head of its “health security” subdivision. He continues to hold this position today under Spahn’s successor Karl Lauterbach. It would prove to be, as Schreyer says, “a key position in the Corona crisis”.

According to one would-be insider account of the German government’s Covid crisis management, which is cited by Schreyer, Rottmann-Großner was calling for a hard lockdown already on February 24^th 2020, at a time when the RKI still assessed the risk from Covid as “low”. He would turn up in a more conspicuously public role in September of last year when the Ministry of Health sent him to serve as Lothar Wieler’s “minder” during the former RKI chief’s testimony on the Covid response in the regional parliament of Brandenburg.

It thus appears clear that Rottmann-Großner has played an important and eminently political role in the context of Germany’s COVID-19 response. He has, incidentally, no medical or scientific background, but merely a BA in politics and economics. Much the same could be said of his boss during the Covid years, then German Health Minister Jens Spahn, whose highest degree obtained is an MA in politics.

But just why Rottmann-Großner should be suspected of having represented anything other than German interests in this connection is anybody’s guess.

And what about the WHO? Schreyer writes that at the same time as Rottman-Großner was calling for more radical containment measures in internal German government deliberations in February, the WHO was also increasing the pressure. Schreyer says that it would increase the pressure even further following its March 11^th pandemic declaration, even though, per the RKI’s assessment, the epidemiological situation in Germany had not changed.

But was it really the WHO which was exerting pressure on Germany and not rather Germany which was exerting pressure on the WHO? Indeed, in those early months of 2020 was it even possible to distinguish between the two?

Here are a few facts about the relationship between the WHO and Germany which Schreyer does not mention:

1. In 2020, Germany virtually overnight became the top funder of the WHO: a position which it would maintain throughout the official pandemic years. More importantly and more to the point, it would be far-and-away the top funder of the WHO’s COVID-19 response. If in 2020, German funding alone represented nearly one-third of the WHO COVID-19 response budget, in 2021 it would rise to nearly 40% and the combined contribution of Germany and the German-dominated EU, under the leadership of former German Minister of Defence Ursula von der Leyen, would represent almost half the budget. (It may well have been more than half, since in 2021 a conspicuously large portion of the response budget is listed as coming from “miscellaneous” sources.) No other country’s contribution even came close. The USA, for instance, only provided around 1/10^th of the German total. The Gates Foundation, whose supposed influence has received far greater attention, provided veritable peanuts by comparison, representing less than 1% of the total budget or around 1/50^th of the German contribution. (For exact figures and a discussion, See my ‘Gates or Germany? Who “Owns” the WHO’s Covid-19 Response?’)

2. The Chef de Cabinet of WHO Director-General Tedros Adhanom Ghebreyesus in this period was the German epidemiologist – and current German foreign office official – Bernhard Schwartländer. Believe it or not, before becoming Tedros’s chef de cabinet in July 2017, just two-and-a-half years before the official start-date of the COVID-19 outbreak in Wuhan, Schwartländer had been none other than the WHO representative in China. He presently holds the title of “Global Health Envoy” in the German Ministry of Foreign Affairs. Astonishingly, per his X profile, as seen below, and other documentary evidence (here and here), Germany’s “Global Health Envoy” is based at the German embassy in Beijing!

When, for instance, in late January/early February 2020, Anthony Fauci, Jeremy Farrar and other Anglosphere scientists and public health officials became concerned by signs that SARS-CoV-2 may have been genetically-engineered and wanted the WHO to lead an investigation into its origins, it was to “Tedros and Bernhard” that they appealed, as the below FOIA’d e-mail makes clear.

“Tedros and Bernhard” would indeed assemble a team to investigate the origins of the virus. Its, in the meanwhile, much-maligned investigation would dismiss the possibility of a lab-origin out-of-hand, focusing almost exclusively on possible zoonotic origins.

The presence of Peter Daszak of the US-based EcoHealth Alliance in the investigation team has drawn a great deal of comment from English-speaking observers. But the team also included Marion Koopmans: the Dutch virologist whose close association with the German PCR-protocol designer Christian Drosten I have discussed here.

More to the point, it also included an official of none other than Germany’s Robert Koch Institute! The RKI member of the team was Fabian Leendertz: a veterinarian and then head of the RKI’s “Epidemiology of Highly Pathogenic Microorganisms” research group. He is presently the director of the newly-founded Helmholtz Institute for One Health in Greifswald.

3. This brings us back to Leendertz’s then boss at the RKI, Lothar Wieler. Like Leendertz, Wieler is a veterinarian. Like Leendertz, he is a proponent of the “One Health” approach to public health, which places emphasis precisely on the risks for human health originating from the animal kingdom.

As Schreyer knows (because he has touched upon it here), in the period in question, no other public health official in the world had better connections to the WHO: namely, because, Wieler was in fact himself a key player at the WHO. As documented in the below excerpt from his current CV on the website of the German National Academy of Sciences (Leopoldina), Wieler was serving as a member of no less than three WHO committees and as the chair or co-chair of two of them.

Most importantly in the present connection, he chaired the committee charged with reviewing the WHO’s International Health Regulations in light of the COVID-19 Response. The revision of the International Health Regulations has formed part of the same process as the more highly publicized “Pandemic Treaty”, which, incidentally, as I show in my article here, Germany has also spearheaded.

As detailed in the same article, the WHO “Pandemic Hub”, which was inaugurated in Berlin in September 2021, is nothing less than a full-fledged joint-venture between the WHO and Germany’s Robert Koch Institute. The hub was created with $100 million in funding from the German government, and the Charité University Hospital of one Christian Drosten is also a partner.

4. And, finally, we should not forget the aforementioned Christian Drosten. The WHO would famously adopt Drosten’s PCR protocol as the “gold standard” in Covid testing. Even before the protocol had been “validated” by the EU-funded journal Eurosurveillance in an infamous 24-hour “peer review”, the WHO had already published two earlier versions of Drosten’s protocol on its website. Astonishingly, the earliest version is dated January 13^th 2020, i.e. just two weeks after the first official reports of the illness in Wuhan! (Some have questioned whether it was also published that day, but, in any case, as a reference in another Eurosurveillance article shows, it was accessible on the WHO website by January 17^th at the latest.)

As the below excerpt from March 9^th 2020 correspondence between the US Department of Health and Human Services and the WHO makes clear, by early March, the WHO was shipping hundreds of thousands of PCR test kits manufactured by Berlin-based TIB Molbiol. TIB Molbiol is the company of Drosten-collaborator and PCR-protocol co-author Olfert Landt.

Why was the WHO in such a hurry to adopt the notoriously over-sensitive Drosten-Landt test? Could the hundreds of millions of euros which Germany would start pouring into the organisation have had something to do with it?

But what interest could the German government possibly have had in exaggerating the threat represented by COVID-19? Well, this would be blindingly obvious were it not for three years of incessantly saying “Pfizer” when the actual owner and legal manufacturer of the mRNA “vaccine” which was the cornerstone of the response to the threat is the German firm BioNTech! As I already showed in detail in my first article on the subject in November 2021, the German government had long been BioNTech’s state sponsor and would directly sponsor its vaccine candidate.

Even if others may somehow remain befuddled about just whose drug the so-called vaccine really is, in Germany, at any rate, there has never been any doubt. “Invented in Germany, made in Germany,” then German Minister of Health Jens Spahn would say proudly during the inauguration of BioNTech’s mRNA manufacturing facility in Marburg on April 1^st 2021.

Indeed, as Spahn noted to in his speech, not just one, but two of the three mRNA companies which had been in the running to produce a COVID-19 “vaccine” were German: the other being CureVac. Both were sponsored by the German government. The German government would even directly invest in CureVac in June 2020, thus ensuring the company’s continued existence in case its vaccine candidate did not get the nod – which is exactly what came to pass.

The BioNTech facility in Marburg, Spahn continued, would be a “starting point for the Federal Republic [of Germany] as a whole” – these were his exact words – namely, for making the country into what he called a “vaccine hub”. “We would like to become an mRNA hub…for the world and for Europe”, Spahn said unabashedly – sounding more like a minister of the economy than a minister of health!

Spahn fondly recalled his first meeting with BioNTech CEO Ugur Sahin “around twelve months ago” and their discussing how the German government could support the company’s vaccine project. Around twelve months ago? Well, that would take us back to precisely the time when the RKI, according to Schreyer, was raising its assessment of the COVID-19 threat level without scientific justification, based merely upon ramped-up PCR-testing.

Was all of Germany turned into a stage for Covid theatre, with 80 million Germans forced into the role of extras, all in order to help realize the “dream” (as Jürgen Kirchner has put it) of “vaccine hub Germany”?

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PANDA focuses on open inquiry, sense-making of scientific developments and the drivers influencing our societies, with an emphasis on the events that mark the Covid era. We invite submissions of articles from experts, enthusiasts, and professionals willing to share their expertise. Please submit your articles to our editorial team at media@pandata.org.

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Watch on Rumble

Cognitive scientist Mark Changizi on understanding what happened during Covid in the context of our evolution.

Mark is a theorist aiming to grasp the ultimate foundations underlying why we think, feel and see as we do. His research focuses on "why" questions, and he has made important discoveries such as why we see in colour, why we see illusions, why we have forward-facing eyes, why the brain is structured as it is, why animals have as many limbs and fingers as they do, why the dictionary is organized as it is, why fingers get pruney when wet, where emotional expressions came from, and how we acquired writing, language and music.

Research links:
Cognitive Scientist - https://www.changizi.com
VINO OPTICS - http://vinOOptics.com

WEU, for those that are unaware, is an acronym for “Weekly Epidemiological Update.” It was the title that the World Health Organization (WHO) gave to their Covid reports. The WHO started generating these weekly reports on 17 August 2020 after giving up on their idea of daily reports. A full 158 WEU reports were produced before zeal for the project petered out. Recently an article was published in the BMJ which was designed to provide “an in-depth analysis” of the “process, from data collection to publication, focusing on the scope, technical details, main features, underlying methods, impact and limitations” [1].  That article has 52 names in its list of authors, 51 of whom were affiliated with the WHO. The remaining co-author was from Johns Hopkins University (JHU).

The article begins with a “Summary Box” that contains five bullet points, each a conclusion based on their analysis of the WEU reports. Since we respectfully disagree with all five points, we thought it appropriate to write a formal response.

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 Bullet 1:

COVID-19 has posed an unprecedented global health crisis, demanding timely, reliable information on the pandemic’s progression to inform the public and guide decision-making.

They expand on this in the body of the article by saying: “The COVID-19 pandemic has posed an unprecedented global health crisis, with more than 770 million confirmed cases and over 6.9 million confirmed deaths being reported globally to WHO from 1 January 2020 to 1 September 2023.”

While we certainly do not begrudge the WHO for deciding to keep tabs on a disease that potentially claimed 6.9 million lives over the space of 44 months, we take exception to calling it an “unprecedented global health crisis.” The word unprecedented means “never having happened or existed in the past” [2]. We are at a complete loss to see how Covid was a global health crisis, let alone an unprecedented one [3]. To be thorough, we demonstrate that there was nothing unprecedented about Covid by pointing out a few things that happen during every normal 44 month period:

• 89 billion cases of respiratory infections [4]

• 2.5 billion people are infected with the norovirus [5]

• 264 million people die of old age [6]

• 37 million people die of cancer [7], and

• 5 million people die in a car accident [8]

Therefore, even taking their numbers at face value, the only thing that was unprecedented about Covid was the response to it. Never before in the history of humanity was nearly the whole world locked down in a vain attempt to arrest the spread of a disease. The “global crisis” of the past four years was not a respiratory disease but a bureaucratic disorder. The crisis was man-made, inflicted on humanity by governments and public health officials that chose to lock people in their own homes, prohibit them from earning a living, close churches, mask children for hours a day, ban weddings and funerals, isolate children from their dying parents, ticket people for walking in a park, and then, finally, to coerce the masses to have an untested pharmaceutical product injected into their arms. Every one of these decisions was unprecedented and nearly global in extent. These responses destroyed healthy businesses, killed poor children, increased stress, weakened immune systems, drove many to the brink of insanity, and in general caused more havoc and death than Covid ever would have. Excess deaths in most countries around the world during the past four years prove this terrifying reality [6].

The fact that the authors never mention any of this, but instead call Covid the unprecedented global health crisis is an embarrassment to the medical professional. The glaring omission makes the WHO a serious contender for itself becoming an unprecedented global problem. If the WHO was truly concerned about public health, then instead of producing weekly updates tracking Covid they should have been producing weekly updates tracking the unprecedented human carnage caused by lockdowns and vaccines [9].

Bullet 2:

The WHO’s COVID-19 Weekly Epidemiological Update (WEU) provided regular, comprehensive and authoritative analyses of the global COVID-19 situation.

Actually, the WEU reports were neither comprehensive nor authoritative. For example, in Figure 1 of their article they provide six graphs, each showing Covid cases and deaths in a different region of the world: Europe, Americas, Mediterranean, Asia, Africa, and the Pacific. Of significance is that in Europe and the Americas there is an almost complete disconnect between cases and deaths, whereas in the other four regions deaths rise and fall in sync with cases. As it is impossible for Covid to have changed its lethality simply by crossing a border to a new country, the graphs are proof that the data was anything but “comprehensive.” We have previously described at length the phenomenon of how the Covid data varies significantly by country [10, 11]. The WHO reports, however, are strangely silent on this glaring trend. How then can they be called “authoritative analyses”?

Bullet 3:

The production of the WEU included several steps, which were standardised, regularly refined and automated when possible, to ensure consistency and accuracy.

The steps taken by the WHO to generate each WEU may have ensured consistency, but it is certain that the results were not accurate. For instance and by their own testimony, they admit to enormous gaps in their data:

• “During this period, reporting completeness varied significantly.”

• “The number of countries reporting  data on new  hospitalisations ranged from 41% to 12%.”

• “The number of countries reporting  data on ICU admissions ranged from 17% to 4%.”

• “11% [of countries] reported weekly new hospitalisation data at least 80% of the expected times.”

• “9% [of countries] did the same for new ICU admission data.”

Furthermore, in the last published WEU it says:

“Please note that the absence of reported data from other countries to the WHO does not imply that there are no COVID-19-related hospitalizations in those countries. The presented hospitalization data are preliminary and might change as new data become available. Furthermore, hospitalization data are subject to reporting delays. These data also likely include both hospitalizations with incidental cases of SARS-CoV-2 infection and those due to COVID-19 disease.” [12]

Therefore, not only do they admit to a lack of data and to delays in reporting, but also to the fact that the data they did receive may or may not be indicative of Covid at all. 

Naturally, this lack of data is entirely expected, for it typically takes many months or years even for technologically advanced countries like Canada and the US to produce good data [13]. What is unexpected, however, is that despite such glaring holes in the data, the WHO should still claim their reports were accurate. Precise, maybe, but certainly not accurate.

If a majority of countries were not reporting any data, how could the WHO generate anything even remotely useful? Indeed, if only 9% of countries reported at least 80% of the time, how can it possibly be said the WEU reports were accurate? It cannot. They weren’t.

Furthermore, although it is not said expressly, there is an important reason why the above quotations discuss “hospitalizations and ICU admissions” rather than “cases and deaths.” The reason is that the WHO used different sources for each. Hospitalization and ICU data were obtained from health authorities, but case and death statistics were extracted from “data repositories” (See Figure 5 in [1]). In other words, despite the fact that only 9% of countries reported their data to the WHO, the WEU could still report cases and deaths in 100% of countries because the data came from a different source. The fact that the WHO did not even claim to be getting its case and death data directly from each country testifies that countries around the world were unable to produce this information. 

If countries were unable to generate case and death data, where did the data repositories get this information from? The short and blunt answer is that they made it up.

Indeed, no information is provided in their article about which repositories were used. However, the obvious and primary data repository would have been the one maintained by Johns Hopkins University (recall that one author of this WEU analysis was from JHU). Based on our recent study of the JHU dashboard [13], all the evidence strongly suggests that case and death numbers in their data repository were primarily generated by computer simulations rather than observed events. This WEU analysis confirms our conclusion, for the very fact that the WHO did not (and could not) obtain case and death data directly from each country, and that only 9% to 11% of countries could report on hospitalizations or ICU admissions, means that factual data was positively unavailable. 

Therefore, the WHO did not have access to case or death data, let alone hospitalisation or ICU admission data. It is remarkable that the authors of this WEU analysis would neglect to discuss this critical aspect of their data. It is disturbing that despite these glaring problems and holes they insist that their data was accurate.

Bullet 4:

Addressing the persisting challenges inherent to the global surveillance of COVID-19, many of which were exemplified by the WEU, will require sustained international collaboration, commitment and investment.

What this fourth bullet means is that the WHO thinks that governments around the world need to invest more money and time into their continued surveillance of Covid. In the body of the article this is explained as follows: 

“It is critical that countries maintain core SARS- CoV- 2 surveillance capacities and activities to inform ongoing public health measures and ensure that  future surges of COVID- 19 are rapidly detected, allowing for swift action to prevent larger outbreaks.”

There are multiple problems with this statement. In the first place, the “health measures” that were already tried (lockdowns, masks, vaccines), besides causing enormous economic damage, mental stress, and deaths, failed to change the course of Covid. The failure of lockdowns has been proven by hundreds of different researchers [9]. And concerning the Covid shots, the authors themselves admit that the vaccine effectiveness (VE) waned over time: “For both Delta and Omicron… VE for individual vaccines [declined] over time for severe disease, symptomatic disease and infection.” What they fail to admit, however, is that the initial high VE and also the subsequent decline were nothing more or less than a mathematical result of the way vaccinated individuals were classed as unvaccinated for a period of time [14]. In plain English, the shots were a colossal failure at preventing Covid [15]. Moreover, the Covid shots have caused an unprecedented number of severe adverse events, including at least one million deaths [16]. If the “swift action” that was taken in the past failed to arrest Covid, what then is proposed to stop future outbreaks?

In the second place, because data takes so long to collect it is functionally impossible to “rapidly detect” surges of any particular respiratory ailment. This is especially true for a disease like Covid whose symptoms are indistinguishable from several other respiratory illnesses [17]. Nor will it help to rely on genomic sequencing to resolve the conundrum. The authors themselves prove this, affirming that:

“Timely  sharing of genomic sequencing data from countries was also challenging due to the median lag between the collection and submission of SARS- CoV- 2 sequences to GISAID varying significantly across countries, ranging from approximately 2 weeks to over 9 months.”

If it takes several months for genomic sequences to be uploaded, by the time surveillance has determined anything of value, the surge, if there ever was one, will be long past.

Bullet 5:

The methodology and lessons learnt from the experience of the WEU offer a blueprint for the development of information products that can support the response to future major health emergencies.

Since there wasn’t a major health emergency in the first place, it is doubtful if the WEU experience can support any future response.

We respectfully submit that the WEU experience should have taught the WHO to be more cautious about declaring a pandemic, because in doing so millions of lives were ruined and lost without cause [18, 9]. It should also have taught the WHO that rapid global data of a specific disease is impossible to collect, even in a high tech world. And it should have informed the WHO that transparent cost/benefit analyses should be done before sweeping measures are recommended on an unsuspecting world. Unfortunately these important lessons seem to have escaped the authors. 

The question is why?

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This is the last of the series of three papers that examine the fact checking industry. Part one showed the areas not yet covered by American journalists that are exploring the Twitter files, Part two explored characteristics of the industry – its geographic spread, and its reach across all forms of media, and academia. This section outlines the extension of the fact check industry to media literacy training. It goes on to reveal funding sources of the fact check industry, in Western countries and further afield, and the impact this well-funded industry has, and may have, on other parts of the world. 

Fact-checking and media literacy training

Many fact check platforms offer media literacy and fact-checking courses: their main targets are children, students and journalists, although courses may be offered more widely. For example, Logically and NewsMobile joined forces with Facebook to offer a media literacy programme in India in 2021 [1]. Africa Check, its main funders being Meta, Bill and Melinda Gates Foundation and Google, trained 4,500 journalists in fact-checking [2]; operates a media literacy programme in schools [3]; and trained over 10,000 people in media literacy [4].  In 2020 AFP ran a Covid-19 media literacy course in France with the support of Facebook [5], AFP in conjunction with Google, runs worldwide online fact-checking training courses for journalists and produces training videos [6].  In Indonesia, Google has joined with AJI, a journalists’ association, to train trainers in teaching media literacy [7]. In USA, Associated Press (AP) announced that Microsoft is supporting media literacy programmes [8]. Google joins with partners MediaWise, part of the Poynter network, and The News Literacy Project to support media literacy training in US schools and the community for English and Spanish speakers [9]. Google has also donated EUR25 million to the European Media and Information Fund for media literacy training.

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Encouraging audiences to think critically, understand nuances and potential inaccuracies, and to avoid online scams and dangers is commendable, but courses on offer have a narrow focus. For example, a media literacy course for seniors, funded by Meta and operated by MediaWise, limits trustworthy sources to organisations such as mainstream media, Wikipedia, WHO and the Center for Disease Control (CDC)[11]. The course facilitators claim that those who spread other narratives, described as ‘misinformation’ or ‘disinformation’, are doing this for financial gain such as advertising revenue and clickbait, or they may be linked to foreign or harmful sources. This and other courses, such as AFP’s fact checking courses for journalists [12], advise the importance of checking out the source by up-reading (finding out where the information came from) or lateral reading (checking what other sources say about the author or the website/news site). This is problematic because when checking out dissenting voices, despite having appropriate qualifications or experience to join the debate, they are often smeared on internet websites [13]. Being able to listen to a variety of thoughtful and educated views on science, health and other issues should be normal in a free society, without prior judgement of credibility.

For example, an AFP course for journalists entitled “Identify trustworthy sources on health topics and evaluate studies” [14] gives useful detail on analysing academic papers, but emphasises the trustworthiness of a small number of elite medical journals and peer review. However, AFP does not acknowledge the influence of pharmaceutical companies and questions of possible bias in medical journals and peer review, as described by retired editors of both the British Medical Journal [15] and the New England Journal of Medicine. Many academics have seen this trend significantly worsen since 2020 [17, 18, 19], whilst issues of flawed data in peer reviewed and published papers that support official narratives are sometimes ignored [20]. Hence journalists cannot rely on medical journals nor even peer review when investigating health issues, and “trustworthy sources” identified by AFP do not give a full picture of the range of useful academic opinions.

Funding at home and overseas

When media organisations have a fact-checking arm, this is usually funded by the media company. By comparison, independent fact checkers that do not produce a product almost entirely rely on donations, grants and earned income from third party fact-checking contracts, training courses, or designing fact check tools. As revealed in the Twitter files, disclosed grants and donations listed on the websites of international fact check platforms are usually from large institutions associated with Western governments; large Western foundations or trusts; EU, American, European and UK governments; large media corporates; Google; Meta; IFCN; or UN departments. Earned income is usually from large corporates, such as Microsoft, Google or Meta. The sums involved are significant. For example, Full Fact received just under GBP500,000 from Facebook, and around GBP200,000 from Google in one year [21]; whilst IFCN received a grant of USD13.2 million from Google to cover a five year period [22]. 

Governments directly fund content moderation, demonstrated by the Twitter files [23] and subsequent investigations [24], but others also reveal government payments, including investigations by UK’s Big Brother Watch [25] and McEvoy and Curtis [26]. The EU requires moderation of social media content, as described in the EU Code of Practice on Disinformation [27]. Logically, a for-profit fact check platform, states that it has been contracted to undertake work for various governments [28]. It is clear that American, British and other governments are directly and generously funding fact-checking activities in order to encourage domestic content moderation in line with their preferences. But governments also pay for content moderation internationally, such as the US-funded National Endowment for Democracy (NED) [29] that funds many fact check platforms including Rappler in the Philippines, and South Asia Check in Nepal; and the EU funded European Endowment for Democracy (EED) [30] that funds Fact Check Georgia and Raskrinkavenje Bosnia-Herzegovina and others. The UK’s Open Information Partnership [31] funds a number of fact check platforms and NGOs in Eastern Europe, especially those who take an anti-Russian line. Many overseas platforms are also directly funded by Western embassies. 

Western social media companies support fact checkers globally. For example, the Indonesian fact check platforms Mafindo and CekFakta members are funded by Google [32]. Meta, Facebook’s parent company, subcontracts fact-checking in 119 countries, with 253 contracts for its third party fact check programme in May 2023 [33]. Facebook only accepts IFCN-verified signatories as fact check partners [34], giving a lucrative initiative for fact check platforms to comply with IFCN’s system. Facebook’s fact-checking programme crosses international boundaries, its contracts demonstrating that content moderation via neighbouring countries occurs around states that are not perceived as friendly to Western interests – inevitably, such interference by outside actors may be seen as hostile or unwelcome. 

Another form of foreign-earned income by fact check platforms is through the design of fact check tools, such as a $2 million grant received by Africa Check from Google [35]. Tools include products that monitor text to find target words or phrases; turn spoken words into text for analysis; predict current and future trends in conversations online and offline; analyse visual images; identify whether text has been altered; and check whether stories, statements or pictures have been used previously. 

In Western countries, the negative effect of censorship include accusations of illegally hacking sites and distorting information obtained to smear those with different opinions [36]; demonetising activities [37], and the removal of accounts [38]. Expressions of similar concerns were not found in other areas, but there are indications that Western influence on international mainstream media and social media is causing concern. For example, one Indonesian study found that newsroom editors thought the local media had been ‘colonised’, with Google now being one of the most important news values that influence story selection [39].  Moreover, India and Georgia have openly expressed concern relating to Western influences via fact-checking within their national boundaries, and have attempted restrictive measures [40, 41].

Conclusion

The recent growth of the fact check industry, its global networks, the widespread annual conferences of fact checkers and media literacy campaigners [42], and the industry’s wealthy funders are still hidden from most of the world’s view. However, fact checkers and current media literacy campaigns challenge freedom of speech and the ability of populations to make informed decisions based on evidence from a number of diverse sources. Fact checkers do not have a democratic mandate, nor any satisfactory appeal mechanism so that those who are censored can challenge their version of what is ‘true’. The Twitter files have made a significant contribution to exposing the ‘Industrial-Censorship Complex’, but examining the whole of the fact-checking industry and its worldwide presence, its reach to all forms of media, its media literacy and fact check activities, and Western funding of content moderation across the world should be cause of great concern for global freedom, sovereignty, and independence. 

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There are multiple events that happened in January 2020 that are, to put it mildly, peculiar. One of them is that only 23 days after China reported that they had found a few cases of an “unknown pneumonia” in the city of Wuhan, three people in Baltimore Maryland launched a dashboard that was designed to track the number of cases and deaths of this disease in every country in the world. All three were connected with the Department of Civil and Systems Engineering at John Hopkins University (JHU). In their own words, the dashboard “was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds” [1]. Along with the dashboard they also maintained a public data repository of cases and deaths [2].

The January 22 launch happened so early on in the Covid timeline that the first WHO situation report had only been released the preceding day, and the term “Covid” had not even been coined yet. In that first WHO report it was announced that “282 confirmed cases of 2019-nCoV have been reported from four countries including China” [3]. Total cases outside of China were only four, and there had been zero deaths. In fact, only six deaths were officially linked to the virus by this date, and all of them were from Wuhan. For comparison purposes and to put things into perspective, the norovirus is estimated to infect 685 million people and cause 212,000 deaths every year [4]. We are not aware of a norovirus dashboard anywhere in the world. 

Odd as it might be that a team at JHU chose to create and launch a dashboard for an unnamed disease despite such low numbers of cases and deaths in so few countries, there are possible explanations for why they did so. Only 3 months earlier JHU had hosted Event 201, a “training tabletop exercise… based on a fictional scenario” of a novel coronavirus causing a global and deadly pandemic [5]. Furthermore, the lead author of the dashboard (Professor Lauren Gardner) is a specialist in modelling infectious diseases. Notwithstanding what these facts might suggest, the dashboard was, at least according to their own testimony, the result of a “spur-of-the-moment” decision and took “just a few hours” to build [6]. 

Despite this hasty beginning, the new website [7] certainly garnered a lot of attention in short order, rapidly becoming the premier data site for media outlets, medical researchers, health authorities, and the general public, not only in the US but also around the world [8, 9]. Within two months of its launch, the website was reportedly being “accessed 1.2 billion times per day,” [10] or nearly half the traffic of the internet giant Google. Within two years it had been cited by medical researchers in more than 8,500 articles [11].

“The Hopkins dashboard has become a ubiquitous and trusted reference point, cited by U.S. federal agencies and major news sources” [12].

Given how much influence the JHU dashboard exerted on the world during the Covid event, it is appropriate to examine it in some detail. In particular, we here discuss some of the difficulties of obtaining data in real-time, the sources they used to obtain that data, how they put data into their system, the language barriers involved in the process, and, finally, the role that computer simulations may or may not have played. The article is rounded out with an example from one particular city, namely New York.

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Obtaining Data in Real-time

Even with the help of modern computers, it typically takes many months (or even years) to produce reliable mortality data. For example, the latest year for which official all-cause mortality figures are available in Canada is still only 2020. The national statistics office, StatsCan, is quick to point out that “reporting delays” and “incomplete data” are the reason behind the three year effort [13]. If it currently takes two or three years for a federally funded organisation such as StatsCan to publish mortality data, how was it possible for JHU to get Covid death data in real time? 

It is equally challenging to produce statistics about one particular pathogen or virus. For instance, seven months after the 2017/2018 flu season had ended, the CDC in the US was still only providing estimates for cases and deaths. Their stated reason was that “data on testing practices and deaths from the 2017-2018 season” were not yet available [14]. The date of that statement was 22 November 2019. Exactly two months later the JHU dashboard went live, promising information on cases and deaths in real-time of a newly found respiratory virus that is similar to the flu. If it takes months or years to obtain mortality and case data for the flu, how was it possible to obtain data for Covid in real time? If it was not possible to do this in 2019, how did it suddenly become possible to do it in 2020?

Naturally, there are plenty of countries in the world where data reporting is unreliable at the best of times. Likewise, the Human Mortality Database, which tracks mortality by country, is regularly ten years behind for many countries [15]. How then did JHU expect to be able to plot Covid deaths for every country in the world in real time? More crucially, how did they possibly update their dashboard “every 15 minutes”? [1, 16]

Data Sources for the JHU Dashboard

Good data depends on good sources, and the only way to understand how the JHU dashboard obtained their data in real time is to examine their sources. Of significance is that their sources changed over time. Initially, their primary data source was “DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise” [1]. This source, therefore, was a combination of news and government reports.

Eventually the team at JHU felt that DXY was taking too long to obtain and publish data, so they branched out to include other sources: “To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard.” As health agencies around the world set up their own dashboards, JHU incorporated them into their list of sources [2].

JHU therefore acknowledged three primary sources of information other than official government websites:

• Twitter feeds

• online news services

• direct communication sent to the dashboard

To the best of our knowledge, no details are provided about any of these three categories of sources. All three have the potential to contain incorrect, exaggerated, or entirely fabricated information. None are either publicly accountable or subject to independent verification. No specifics are provided about who could or did communicate directly to JHU.

Scraping the internet for news stories about Covid certainly has the potential to speed up the process of collecting data. Unfortunately, and beside the above named problems, it also introduces the possibility of a noise-amplifying feedback loop. After all, if “major news sources” were trusting JHU for accurate data, how could JHU be getting their data from (potentially) the same news sources?

Duplicates

Furthermore, even assuming that all the sources used by JHU were accurate, the task of “combining multiple data sources is a complex process” [17]. One notable challenge is how to handle duplicates. Whenever information is obtained from more than one source it becomes possible that the same event is counted twice. News outlets, after all, do not include health identifiers of the people mentioned in their stories. How then did JHU de-duplicate the data? Does this mean that Covid case and death counts as reported by the dashboard may have been two, three or many times higher than reality in some regions? Indeed, there are occasions when users of the data were convinced the dashboard did contain duplications [18]. For example, on 11 March 2020, one user of JHU’s data repository wrote: “There is a challenge with inserting duplicate data into a stream that never contained it before. Reporting both at the state level and the city level in the same column is bound to cause problems” [18a]. To which another user replied, “CSSE appears to double count cases and deaths” [18b]. CSSE (“Center for Systems Science and Engineering”) is the acronym for the department at JHU that managed the data repository.

Worldometer as a Data source

JHU expressly mentions Worldometer as one of their sources [2]. Worldometer themselves confirm this fact, saying: “Our data is also trusted and used by… Johns Hopkins CSSE” [19]. In general, Worldometer uses computer simulations to report statistical information in “real-time.” Their simulations are based on yearly totals and computer estimates. For instance, if one million people die in motor vehicle accidents every year, then on average one person perishes every 31.6 seconds. The Worldometer dashboard for accidents would simply add one new death accordingly whether or not anyone actually died during that amount of time. Naturally, Worldometer would have no way of knowing if anyone did die.

Did Worldometer use computer calculations to determine Covid statistics in a manner similar to what they do for motor vehicle accidents? If they did (and because they could not know the yearly totals in advance), did they use epidemiological models rather than real deaths to estimate how many people were going to die that year from Covid? Neither question can be answered with certainty. Their list of sources only includes government institutions [19]. However, since a full list of sources is not available, and since they do not expressly deny using computer algorithms, it is quite likely that Worldometer did use computer models to generate their Covid statistics. This is made more certain by the fact that governments would not have been able to generate Covid information in real-time.

In May of 2020, CNN published an interesting news article that highlighted the confusing nature of the relationship between the JHU dashboard and Worldometer [20]. The responses from JHU to questions that were asked of them in preparation for that article were somewhat evasive, and left more questions than answers [21].

The fundamental question that remains unanswered, of course, is why would JHU use Worldometer as a source in the first place? Since JHU used Worldometer as a source, does that mean that the underlying data on the JHU dashboard was also based on computer models rather than real events? Part of the reason why it is so difficult to answer any of these questions is that the code used by the dashboard was not open source, a complaint frequently levelled against the platform by users [22]. Another reason is that data was often input into their system without either explanation or verifiable references.

Inputting Data into the System

According to the Lancet article by Gardner et al, for the first ten days after the launch of the JHU dashboard, “all data collection and processing were done manually, and updates were typically done twice a day” [1]. From a logistical perspective this was doable since there were so few cases and only a few countries involved during this period of time.

Concerning accuracy of the data, they claimed that “[b]efore manually updating the dashboard, we confirm the case numbers with regional and local health departments…as well as city-level and state-level health authorities.” It has already been established, however, that neither Canada nor the US could produce either mortality or influenza data within a six month time frame. How then did not only Canada and the US, but eventually every country in the world, provide daily case and death counts that JHU could use for verification?

In an attempt to answer this last question, the websites of Statistics Canada were examined to see how they obtained their Covid data. It was found that StatsCan did the same as the JHU team did, for they also used “web scraping techniques to gather relevant data from a variety of websites on COVID-19” [23]. Since no information is provided about which websites were scraped, I emailed StatsCan to obtain a list. Their kind reply stated: “Statistics Canada will not have a comprehensive final list of all potential websites used for web-scraping during the pandemic.” Two reasons were given: first, several divisions were involved, each with different procedures, and second, confidentiality issues. StatsCan suggested we contact the Public Health Agency of Canada (PHAC). However, PHAC was relying on StatsCan for the information, and StatsCan was doing the web scraping, not PHAC [23]. Does this reply mean that StatsCan does not know where they got their Covid data from? Or does it mean they know and are unwilling to share their sources with the public?

The reply from StatsCan leaves one wondering if their information came from the JHU dashboard. Given how popular the JHU dashboard became, that it was expressly designed to enable health authorities to track the outbreak, that it was cited by US federal agencies, that it has been widely used by medical researchers, and that StatsCan scraped the web to obtain their data, it is quite likely that StatsCan did. Even if Canada did not, it is more than likely that some countries did adopt the JHU data as their own. How could JHU confirm their data with health authorities if those same health authorities were getting their data from JHU?

In view of this, there exists the very real possibility that JHU uploaded data to their dashboard, some countries used and published this information on their own websites, and then JHU confirmed their entries by comparing their numbers with those on official government websites. If this happened, it is circular reasoning at its finest. Unfortunately, there is no way to prove if this did or did not happen. Either way, it still leaves us wondering what sources JHU used for their data.

Automatic Updates

Whatever the sources were, manual updates were soon terminated in favour of automation: 

“The manual reporting process became unsustainable; therefore, on Feb 1st 2020, we adopted a semi-automated living data stream strategy.” [1] 

The word “unsustainable” makes it sound like they were being swamped with Covid cases. The actual facts refute this. According to the WHO reports there were only 19 countries reporting cases on this day [24]. This might have involved 80 to 100 entries on the first of February. It is not at all clear how so few entries were considered unsustainable.

The Lancet article asserts that the manual updates were first confirmed with the appropriate health authority. Nothing whatsoever, on the other hand, is said about whether or not automated inputs were ever confirmed.

Furthermore, is it even possible to automate data collection across multiple websites from a growing list of countries, when every website uses different formats to display their data? This question is especially valid given how often these formats were changed during the Covid event. Indeed, as one study group found, as late as May 2021 there were still no “standards” for reporting Covid data [17]. How then did the small JHU team do it when they themselves eventually acknowledged all these problems? [16]

Language Barriers

Two of the three people involved in designing the JHU dashboard were native to China, the third was American. This would have enabled them to read the Chinese reports published on the DXY website. But not every country in the world publishes data in either Chinese or English. The difficulties of extracting data from websites in foreign languages are significant even with automated translation tools. Automating this globally is almost inconceivable. Scraping the internet is nigh impossible when the websites being searched are in a language unknown to the researcher. Challenges of this sort are commonly experienced by anyone doing global research, and as a result researchers often confine themselves to countries that use a language known to them. How then did the JHU team do it?

First out of the Gate

The JHU dashboard was almost always the first website to report the first Covid case in a given location. Gardner claimed that:

“the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions…With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report” [1].

Contrary to what this statement implies, dashboards do not capture this type of information, people do. How is it possible that the small JHU team responded so fast as to find the first new case in almost every single country before anyone else did? Is it only a coincidence that Gardner had recently developed a model that used air-travel patterns to predict exactly this? Writing about that model, she said: “the model provides the expected number of (the 100) imported cases arriving at each airport globally” [25].

Was this model so accurate that it aided JHU in finding each first new case? Given how inaccurate the model was, that scenario is highly unlikely: the same model had predicted case numbers in China that were five times higher than what was being reported. Despite this glaring problem with her model, Gardner still felt that it was more accurate than fact-based reports: 

“We believe the actual number of 2019-nCoV cases in mainland China are likely much higher than that reported to date. Specifically, we estimate there to be around 58,000 cumulative cases of 2019-nCoV in mainland China by the end of January (as of January 31, the reported cases is close to 12,000).” 

Is it not possible, therefore, that Gardner also felt that her predictions of when and where the first cases should occur were also more accurate than what any country was reporting? Did JHU report new cases on their dashboard based on their models? Is this why she said “the dashboard was particularly effective at capturing…”? If so, did other countries, and the WHO, believe what JHU had reported, and subsequently report it themselves?

Computer Modeling as the main source of data

The only answer that provides a rational explanation for all the questions asked thus far is that the JHU dashboard was based on computer simulations rather than observational data. It also appears that from time to time the JHU team obtained empirical data that was then used to adjust and “correct” the output from their models [26, 27]. The reasons why this is the most plausible answer is as follows:

• Governments are not capable of providing data in real time

• It is too difficult to extract data from news media sites in foreign languages

• Dashboard entries were automated

• There was no valid way to de-duplicate the data if obtained from news sources

• The dashboard was updated on short intervals (15 minutes or hourly)

• Sources include Worldometer, a site specialising in computer simulations

• Sources also include “direct communication to the dashboard,” which could have involved data from a computer simulation

• It is not known whether automated entries were confirmed in any way

• The dashboard was designed to provide health authorities with data

• Health authorities trusted the JHU data as accurate

• The repository contains multiple data dumps to “correct” the data

• The dashboard reported the first new cases in a country before anyone else did

Evidence for the use of Computer Modelling

On 4 March 2024 I sent an email to Lauren Gardner (the lead author of the JHU dashboard project) asking whether or not computer models were used at any point for the dashboard, and whether the models were available. Unfortunately, no reply has been received to date. 

Lacking verbal confirmation, and having thus far only found circumstantial evidence, it was necessary to keep digging for perhaps better evidence as to whether or not JHU used computer models to obtain their data. Not surprisingly, the evidence exists. For example, on 13 March 2020 Professor Lauren Gardner spoke at a congressional hearing on Capitol Hill to explain the dashboard. During the presentation she expressly mentioned “modelling efforts that we are doing behind the scenes” [10].

Furthermore, on the JHU website it says:

“Gardner is a specialist in modelling infectious disease risk, including COVID-19….Gardner leads COVID-19 modelling efforts in partnership with U.S. cities to develop customized models to estimate COVID-19 risk at the local level.” [28]

When these two quotes are combined with the fact that Gardner was reported to be so busy managing the dashboard in early 2020 that she had no time to do anything else, it is certain that the modelling work was for the dashboard. Indeed, and as one article pointed out, “working around the clock for 10 weeks straight, they’ve been so consumed with dashboard maintenance that they’ve had little time to analyze the data it actually shows” [6].

It is also worth noting that in 2019 Gardner developed “a novel mathematical modeling framework” for estimating a viral outbreak, a model which was meant to be “calibrated using historical outbreak data” [29]. 

Moreover, the website for JHU’s Center for Systems Science and Engineering states that modelling is one of the foundational pillars of their department. The CSSE department defines itself with these words: “Systems science is a modelling approach that embraces the dynamic interaction of engineered, human-behavioural, and natural components in time and space” [30]. 

Therefore, based on the fact that Gardner is an expert in disease modelling, is accustomed to calibrating models with empirical data, was actively involved in developing models for Covid, has gone on record that her models are more accurate than officially reported numbers, and that her department considers modelling fundamental to approaching any problem, it should be taken for granted that computer models were being used as the underlying source of data. In other words, unless strong evidence exists to the contrary, it is certain that the JHU team was using computer models to generate case and death numbers. No direct evidence has been found however.

In agreement with these sentiments, a survey by Jesse Pietz et al of 25 different Covid dashboards expressly states that the JHU dashboard was using the SIRD (Susceptible, Infected, Recovered, Deceased) epidemiological model in 2020 for simulating the spread of Covid [31].

Finally, an interesting Twitter thread was posted in November 2020 that provides evidence that Ensheng Dong (the undergraduate that built the dashboard) was uploading data to the JHU repository with data created by computer modelling [32]. (Although we regret that the significance of this thread escaped our attention for so long, we do acknowledge that discovery of it formed a significant part of the impetus for the research that led to this article.)

In August 2022, Ensheng Dong et al published a review of their dashboard that discussed some of the lessons learned and challenges faced along the way. The authors assert: “In line with our commitment to open data, the data displayed on the dashboard have solely come from publicly accessible sources” [16]. And a little further on they added, “the dashboard relied entirely on publicly available data.” Does this mean that the dashboard only used fact-based empirical data? Not necessarily. Worldometer is a “publicly accessible source” that was almost certainly based on computer models. Furthermore, some governments used computer models to estimate how many people were going to get sick or die from Covid. The output from these models were also publicly available. And, it is not unreasonable to think that some health authorities were getting their numbers directly from JHU models in the first place. Noteworthy is the fact that they do not say they only used empirical or observational data.

Computer models were used in multiple countries to estimate Covid cases and deaths  [33, 34]. However flawed the models were (and they were terribly flawed [35]), it was always understood that they were only models [36]. The JHU dashboard, on the other hand, claimed to be delivering real-time data of actual Covid cases and deaths. The evidence strongly suggests otherwise.

Confounding the Data

There are multiple problems that will naturally follow if a respected dashboard purports to be hosting empirical data suitable for use by health authorities, but the underlying data is actually being generated by computer models. These problems are guaranteed to persist even if the data is “confirmed” and updated with data from official government websites.

A few of the notable problems that will arise are:

• Some countries, knowing that they are unable to obtain data in real time, will be inclined to trust the dashboard and use its numbers for their own. JHU then “confirms” its own estimates against the “official” numbers. Since the official numbers were based on their own estimates in the first place, the error is confirmed, and neither the JHU data nor the country’s official data are correct. The result will be that Covid numbers in some countries will be as wrong as the computer models.

• Other countries will rely on their own data entirely. When JHU “confirms” their estimates, the JHU data will be corrected. In this scenario the data will be as correct as testing and lab diagnostics allow, or as the data from those countries permits.

• As a result of the above, two neighbouring countries may have vastly different rates of Covid cases and deaths, not because Covid behaved differently in the two countries, but because one country accepted the JHU data as reliable and the other did not. The end result will be that it will be impossible to compare Covid statistics between countries.

• Since JHU data was broken down by cities in some regions of the world, some cities may have accepted the JHU data while others did not. The result will be that Covid statistics may be empirically-based in one city and model-based in another. This will render it impossible to compare one city with its neighbour.

• In those countries where JHU provided city level data, since some cities used the JHU data as their own while others did not, the aggregate values for each province or state may be meaningless.

• The net effect of some regions accepting the JHU data and others not, both at the city, state, and country levels will mean that any computer algorithms used in the JHU models will be visible for some geographical locations but not for others. As a result, in some places the data will fit a SIRD model while in other places it will not. This will render epidemiological studies hopelessly confused.

All of the above-mentioned problems have been central aspects of the many discussions surrounding Covid — not only that we ourselves have engaged in and written about, but also that we have read about or are aware of. For instance, we found from studying Italy that Covid deaths aligned with regional borders rather than with what one would expect from an epidemic of a novel virus [37]. When studying New York City we found that what reportedly happened in that city did not happen in other large cities in the US [38]. When studying mortality patterns around the world, we found that Covid “respected” regional borders in unexpected ways [39, 40]. Therefore the actual problems that surfaced while studying the Covid data aligns with the type of problems one would expect if the JHU dashboard was based on computer simulations augmented by observational data.

An example of how serious it can become when information is believed to be based on real facts though it is based on a computer model is the disappearance of flight MH370 in 2014. The software used by Malaysia Airlines for tracking aircraft led them to believe that the missing plane was over Cambodia. However it was later discovered that “the ‘flight tracker’ is based on [computer simulated] projection and could not be relied [on] for actual positioning or search” [41]. The plane was nowhere near Cambodia, and the delay caused by this misunderstanding was enough to allow the plane to be lost [42].

Example: New York City

Finally, we finish this discussion of the JHU dashboard by examining New York City (NYC) as a case study to demonstrate some of these issues visually. In the first graph below, daily Covid deaths are plotted using data obtained from two different sources: NYC Health (blue line) and the JHU dashboard (red line). The third line (green) is simply the result of multiplying daily JHU values by 4/3. The reason behind this green line is explained below. The graph includes all reported Covid deaths in NYC up until 17 May 2020.

The reason that 17 May 2020 was chosen as the cutoff date for this graph is that NYC Health (NYCH) changed several key aspects of its reporting methods on this day, causing significant disruptions to the JHU trends. It should also be noted that the data used in the above graph was not entered into their respective Github repositories on the day shown. The NYCH data was first committed to the repository on 22 December 2020, after which it was adjusted multiple times over the next three years. The JHU data was entered daily until May 17, then adjusted again on July 1, Aug. 31, and September 1. Other than the fact that the September. 1 entry by JHU was done to distribute deaths in NYC by borough, we are unaware of any explanations for the other adjustments [43].

In general, both NYCH and JHU included what they called “probable deaths” in their daily numbers. Since they kept track of these amounts separately and on a daily basis, we opted to include them in the NYCH daily values (blue line) but not for JHU (red line). In this way, the difference between the blue and red lines in the above graph is on account of “probable deaths.” [43]

The similarities between the blue and green lines provide almost irrefutable proof that the “probable death” numbers were artificially generated on a computer. With a little bit of effort I was able to find a relatively simple equation that makes the match between the two lines all but perfect. However, we leave it for either NYCH or JHU to share what equation they used to generate their “probable deaths.”

We next plot Covid death data (including “probable deaths”) from only NYCH (Fig. 2 below). The smoothness of the curve is remarkable, and almost certainly reflects the use of an SIRD epidemiological model as the underlying source of the data. Naturally, since the JHU curve is identical in shape (only with smaller numbers), it too reflects an SIRD model. Since we have already established that a simple equation exists for “probable deaths,” it is fairly convincing evidence that models were used for all the data: confirmed, probable, and total.

To drive this last point home, and for comparison purposes, we next plot the graph of Covid deaths in Hubei province during the first three months of 2020 (Figure 3 below). Despite the fact that the population of Hubei province (58 million) is seven times that of NYC (8.3 million), the number of deaths in Hubei (peak: 147, sum: 3,164) was significantly less than in NYC (peak: 831, sum: 23,338 ). For these numbers to be correct, it would mean that what happened in NYC was 51 times worse than what happened in the province where Covid supposedly originated. That scenario is so unlikely that it borders on preposterous, and lends support to the notion that the NYC data was not based on observed facts.

It is also observed that the graph for Hubei province (Fig. 3) is more consistent with what empirical data typically looks like (jagged), while the graph for NYC (Fig. 2) resembles  what a computerised model would produce (smooth).

We have previously demonstrated that it is impossible that 23,338 people died of Covid in NYC in spring 2020 [44]. We have also demonstrated that the NYC mortality data is of questionable integrity [38]. Based on the current discussion, we now argue that the most viable explanation for the erroneous data is that a computer algorithm (based on an epidemiological model) was coupled with an improper feedback loop between JHU and NYCH. All of this suggests, and that rather strongly, that Covid death numbers for NYC were invented on a calculator rather than counted in a morgue. We invite either NYCH or JHU to explain why and how these figures should be interpreted differently, and to produce the death certificates of the deceased as proof.

Of course, if Covid death numbers in NYC were based on a computer model, it follows that the residents of NYC were subjected to three months of unnecessary terror in early 2020, being told that thousands of their neighbours were perishing on account of Covid when nobody had (or yet has) any idea how many people were actually dying. It is, therefore quite possible that nothing unusual happened in NYC in the spring of 2020 (even as nothing unusual happened in China [45]), and that the 24% average rise in 911 calls [46] during this period of time, along with the inexplicable and disturbing increase in cardiac arrests [47] were not caused by Covid but by media induced fear [48].

Conclusion

The JHU dashboard was “developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds.” Beside the fact that tracking any illness in real time is functionally impossible, and notwithstanding the fact that nothing special was “unfolding” when they developed it, all the evidence suggests that they succeeded in creating a Covid dashboard by using computerised models that were “corrected” from time to time with data obtained from official government websites. By mixing data from computer models with data from observations while simultaneously asserting that they “relied entirely on publicly available data,” they confounded the data so badly as to render it meaningless. As a result, the JHU Covid database is and was so unreliable that it should never have been used for determining either Covid cases or deaths.

As startling as this conclusion may sound, it is basically what Aaron Katz (a supervisor on the JHU development team) said four years ago:

“Ten years from now we’ll see all the reports and retrospectives that will tell us exactly what happened and where….[But for now,] we’re trying to solve this problem of situational awareness in the present moment” [11].

At the very least his words inform us that it typically takes ten years to get accurate data on a specific illness. At the worst, the quote assures us that the JHU dashboard was unreliable, that the trust placed in the data was entirely misplaced, and that the JHU dashboard caused many wrong conclusions about Covid.

In view of all these facts and observations: 

• We call on governments and statistical offices around the world to return to their former slow but accurate process of collecting reliable data through normal channels,

• We encourage medical researchers to resist the temptation to use Covid data from the JHU repository and instead wait until reliable data becomes available,

• We request John Hopkins University to be fully open and transparent about the computer code, models, sources, and procedures used in their dashboard, 

• We invite New York City to begin a transparent inquiry into if and how their data was corrupted by computer models, and, lastly, 

• We urge the general public to be more critical of any claims asserting “real-time” global statistics of a disease.

The author is thankful for the invaluable assistance provided by colleagues and contributors to this article who wish to remain nameless.

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