Faulty predictions by computer simulation may have promoted ineffective public health policies during COVID-19

Abstract

The world has witnessed many epidemics in the past, some caused by other coronaviruses; and virus in this family cause 15% - 20% of all upper respiratory infections in humans, even in the absence of epidemics. SARS-CoV-2 (the causative agent of COVID-19) had a mortality much lower than those viruses of relevance in biodefense and a sensitivity to environmental sunlight inactivation higher than influenza virus.

The goal of this article was two-fold: a) to provide an answer to what turned a pandemic caused by a rather ordinary virus into an extraordinary public health crisis and b) whether the public health measures elicited by the predictions made by computer simulation were effective. Responding to these questions resulted in unexpected findings regarding the effectiveness of lock-downs and curfews, use of face masks, mandating social distancing and ordering massive vaccination campaigns.

The present work consolidates the most reliable epidemiological data gathered by international databases and governments of several countries with data from pertinent previous publications listed in References.

The data summarized in this article indicates that unusually restrictive measures were mandated largely in response to predictions made by computer modeling of the pandemic. In particular, the predictions reported by the WHO Collaborating Centre for Infectious Disease Modeling, of the prestigious Imperial College of London, projected that without drastic intervention (like lock downs and quarantines), 7 billion infections worldwide and 40 million deaths during 2020 alone. These figures are compared in this article with actual data reported during 2020 and at the end of the pandemic (2023) demonstrating that computer predictions of the evolution of the pandemic were a blunder with catastrophic global consequences.

The analysis in this study corroborates the stational progression of the pandemic which explain why measures intended to prevent person-to-person transmission of the disease (like lock-downs, wearing face masks, and social distancing) should have failed in containing COVID-19. This notion is supported by presented data on the ineffectiveness of lock-downs, use of face masks, and social distancing in selected countries.

The lower mortality registered in developing countries in comparison to developed countries where more hospital beds and respirators were employed, suggests that hospital infections resulting from intensive medical intervention and not SARS-Co V-2 could have accounted for the majority of deaths among patients otherwise healthy and without compounding health conditions.

The data discussed here indicates also that a variety of different experimental vaccines failed to prevent infections among selected countries of South America, and that instead, natural attenuation and progressively less invasive hospital procedures could account for the eventual ending of the pandemic.

Potential lessons to be drawn from the mismanagement of COVID-19 and preventive measures to instrument before the next pandemic are proposed at the end of the article.