New coronavirus pandemic: an analysis paralysis?

In 1854,Dr. JohnSnow laid the foundations of epidemiologyby applying statistical thinking to the investigation of the cholera epidemic in London, but also by acting on it despite the great uncertainty that reigned (Snow 1856). This is a tale known to all epidemiology students, the prevailing theory ofwhichwas that, at the time, cholerawas caused by miasmas – bad smells. Snow carried out the first statistical study, which one would qualify today as “ecological”. He observed that cholera occurred more often among people living in buildings with higher proportions of subscribers to awater pumpdrawing itswater downstreamof a river-borne sewage spill in the Thames, compared to those subscribed to apumpdrawing itswater upstreamof sucha landfill. He thencarriedout a study, the equivalent to a “case-control study” as we called them nowadays, comparing cholera patients to otherwise healthy people (non-cholera sample) at an individual level and checked which pump they were subscribed to precisely. Upon calculating the “odds ratio” that played against the downstreampump, he concluded that cholera wasprobably transmitted through consumptionof sewage-contaminatedwater. Despite his innovative reasoning, Snow did not succeed in convincing his contemporary peers with mere statistics. Of a decisive character – a reputed obstetrician he twice assisted Queen Victoria through childbirth with experimental anesthesia – he removed the handle of the incriminated pump himself, rendering it ineffective. The cholera epidemic resolved soon after. It is only almost 30 years later that Robert Koch convincingly demonstrated that a vibrio, first isolated by Filippo Pacini in 1854, caused the disease (Bentivoglio and Pacini 1995; Howard-Jones 1984). Yet, Snow had demonstrated statistically and empirically, bymeans of action, that the pumpwas the real cause of theproblemat hand, the epidemic. One can draw from this experience that sound epidemiology may be as powerful as microbiology at identifying determinants of diseases when what it actually showed was that epidemiology is good at finding causes of epidemics, without needing to even know the cause of the disease itself. The biggest lesson, in fact, is however often forgotten: the importance of acting under uncertainty and that epidemiology is a science of probability with no real impact if not followed by action. Indeed, a large number of epidemiologists have since become exactly the opposite of what Snow demonstrated. Becoming specialists in identifying uncertainty in any scientific endeavor, epidemiologycanoftenput thebrakesonaction. From this perspective, theunfoldingaccount of the COVID-19 epidemic is deeply instructive. On December 30, 2019, two days after being admitted to hospital with respiratory symptoms, a first case of a so-called “coronavirus-SARS” was diagnosed in Wuhan, known today as the epicenter of the COVID-19 pandemic (Report of the WHO 2020). Launched by the emergency department at Wuhan Central Hospital, the first alert was rebuffed by a staff inspector who instructed the physician not to speak out to prevent alarming