The science we never share risks being a finding we never found. As the pile of unshared science grows, our scientific understanding of crises like pandemics suffers from the attrition of the science it doesn’t know. It should be in the interest of all scientists to facilitate the sharing of scientific ideas to ensure no science goes unshared from fear of ridicule or public execution. 

Early in the Covid pandemic, Michael Levitt noticed a gradual decay of case growth rates over time in Wuhan, and many dismissed or ignored his observations on account of what they viewed were improper credentials and unconventional mathematical methods (Gompertz curves, as opposed to conventional compartmental models in epidemiology).
Some researchers went so far as to call Michael Levitt’s work “lethal nonsense,” saying he was being an irresponsible member of the scientific community by not being an epidemiologist and presenting work that Levitt’s critics believed downplayed the coronavirus.
On March 17, 2020, John Ioannidis argued that Covid severity was uncertain and extreme containment policies such as lockdowns could possibly cause more harm than the pandemic itself, provoking a persistent culture of animosity towards Dr. Ioannidis, from false claims of conflicts-of-interest in 2020 to people accusing Ioannidis of “horrible science” and more.
My Experience as a “Deviant” Epidemiologist
As a mathematical biologist studying viruses jumping from bats to people for a few years prior to Covid, and as a time-series analyst with nearly a decade of experience forecasting by early 2020, I was also studying Covid since January 2020.
I noticed the wisdom of Levitt’s Gompertz curves – Levitt found an observation I myself had found independently, of regular decays in the growth rate of cases well before cases peaked in Wuhan, and then in early outbreaks across Europe and the US. In my own work, I found evidence in February 2020 that cases were doubling every 2-3 days (midpoint estimate 2.4 days) in the early Wuhan outbreak at a time when popular epidemiologists believed Covid prevalence would double every 6.2 days.
We knew at the time that the earliest cases were exposed in late-November 2019. Suppose the first case was December 1, 2019, 72 days prior the approximate early-2020 peak of cases in China on February 11, 2020. If cases strictly doubled every 2.4 days over that 72-day period, as many as 1 billion people, or 2/3 of China, would have been infected. If, instead, cases doubled every 5 days, we’d expect roughly 22,000 people to be infected in China.
If cases doubled every 6.2 days, we’d expect 3,100 people to be infected in China. The slower the case growth rate one believed, the fewer cases they expected, the higher the infection fatality rate they estimated and the more severe they worried the Covid-19 pandemic would be. These findings led me to see the merit in Dr. Levitt’s observations, and to agree with Dr. Ioannidis’ articulation of the scientific uncertainty surrounding the severity of the Covid pandemic the world was about to experience.
However, when I saw the world’s treatment of Levitt, Ioannidis, and many more scientists with contrary views that mirrored my own, I became fearful of possible reputational and professional risks from sharing my science. I tried to share my work privately but encountered professors claiming I was “not-an-epidemiologist”, and one told me I “would be directly responsible for the deaths of millions” if I published my work, was wrong, and inspired complacency in people who died of COVID.
Between these personal encounters from scientists in a variety of positions and the public stoning of Levitt and Ioannidis, I worried that publishing my results would result in me being publicly called not-an-epidemiologist like Levitt, and responsible for deaths like both Levitt and Ioannidis.
I managed to share my work on a CDC forecasting call on March 9th, 2020. I presented how I estimated these fast growth rates, their implications for interpreting the early outbreak in China, and their implications for the current state of COVID in the US. Community transmission of Covid in the US was known at the time to have started January 15th at the latest,
I showed how an outbreak starting in mid-January and doubling every 2.4 days could cause tens of millions of cases by mid-March, 2020. The host of the call, Alessandro Vespignani, claimed he didn’t believe it, that the fast growth rates might just be attributable to increasing rates of case-ascertainment, and ended the call.
Just 9 days after I presented on the CDC call, it was found that Covid admissions to ICUs were doubling every 2 days across health care providers in New York City. While case-ascertainment could be increasing, the criteria for ICU admission, such as quantitative thresholds of blood-oxygen concentrations, were fixed and so the ICU surge of NYC revealed a true surge of prevalence doubling every 2 days in the largest US metro area.
By late March, we estimated an excess of 8.7 million people across the US visited an outpatient provider with influenza-like illness *ILI) and tested negative for the flu, and this estimate of many patients in March corroborated a lower estimate of COVID pandemic severity.
Having watched Levitt, Ioannidis, Gupta and more get mobbed online for publishing their evidence, analyses and reasonings for a lower-severity pandemic, I knew that publishing the ILI paper was an act of deviance in an extremely active online scientific community.
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