When it came to dealing with an unexpected surge in infections and deaths from SARS-CoV‑2 (the virus causing COVID-19 symptoms), federal and state policymakers understandably sought guidance from competing epidemiological computer models. On March 16, a 20-page report from Neil Ferguson’s team at Imperial College London quickly gathered enormous attention by producing enormous death estimates. Dr. Ferguson had previously publicized almost equally sensational death estimates from mad cow disease, bird flu and swine flu.
The New York Times quickly ran the hot news about this new COVID-19 estimate:
The report, which warned that an uncontrolled spread of the disease could cause as many as 510,000 deaths in Britain, triggered a sudden shift in the government’s comparatively relaxed response to the virus. American officials said the report, which projected up to 2.2 million deaths in the United States from such a spread, also influenced the White House to strengthen its measures to isolate members of the public.
A month later that 2.2 million estimate was still being used (without revealing the source) by President Trump and Doctors Fauci and Birx to imply that up to two million lives had been saved by state lockdowns and business closings and/or by federal travel bans.
The following summary of the Ferguson/Imperial College report provides clues about how the model came to generate such dramatic conclusions:
In the (unlikely) absence of any control measures or spontaneous changes in individual behavior, we would expect a peak in mortality (daily deaths) to occur after approximately 3 months. In such scenarios, given an estimated R0 of 2.4, we predict 81% of the G.B. and U.S. populations would be infected over the course of the epidemic… In total, in an unmitigated epidemic, we would predict approximately 510,000 deaths in G.B. and 2.2 million in the U.S., not accounting for the potential negative effects of health systems being overwhelmed on mortality.
This worst-case simulation came up with 2.2 million deaths by simply assuming that 81% of the population gets infected –268 million people– and that 0.9% of them die. It did not assume health systems would have to be overwhelmed to result in so many deaths, though it did make that prediction.
Neither the high infection rate nor the high fatality rate holds up under scrutiny.
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