What the seasonal flu teaches us about dealing with the coronavirus scare

Comparisons of coronavirus infections to the mundane seasonal ‘flu have invited widespread mockery. In fact, the comparison is oddly and usefully apt. Estimates of infection and death rates –for both – require heroic, unverifiable assumptions. And how we deal with flu outbreaks provide useful lessons for coping with what the World Health Organization has declared a global pandemic.
Like many, I never see a doctor when I believe I’ve gotten the flu. Most patients who do see physicians are “clinically” diagnosed, in a rough and ready way. The US Center for Disease Control (CDC) recommends laboratory tests only for hospitalized or high risk patients. And, according to the CDC, “proper interpretation” of tests requires consideration of numerous factors such as whether the specimen is taken from the upper or lower respiratory tract, accuracy of the test used as compared to a “gold standard” test, and the prevalence of the flu in the patient population.
These interpretations (like my personal guesses and physicians’ clinical diagnoses) are fallible: laboratory testing isn’t like dipping litmus paper into a beaker.
Attributing deaths to the ‘flu is also a fallible judgment call, not the unambiguous result of a foolproof test.
How many are diagnosed, whether by themselves, clinically by physicians or nurses, or through lab tests, is also a guesstimate. I keep my self-diagnosis to myself and professional results aren’t entered into a national registry or data base.

With the new coronavirus, the problem of testing mistakes arising from errors in the underlying science, the design and construction of instruments, and, in the interpretation of results are even more acute. We cannot even objectively assess the magnitude of the errors – how could we reliably test the accuracy of a new test for a new disease? And as with new vaccines and drugs, turning promising laboratory research into practically useful tests requires extended trial and error; the recent Theranos scam should warn us about trusting miraculous leaps. Training clinicians to operate and interpret new tests is another huge practical problem as the history of mammography shows.
But, without reliable tests of known accuracy and comprehensive, well-maintained registries, epidemiologists cannot escape classic ‘garbage-in garbage out’ problems of modeling the severity and progression of infections.
Alarmists point to arresting reports of high proportions of fatal cases. According to a report published in the New England Journal of Medicine  of patients in Wuhan – the epicenter of China’s coronavirus outbreak — 1.4% of 1099 “laboratory confirmed” cases had died. This is about ten times the estimated proportion of deaths attributed to flu infections in the US. But the US flu proportions likely include many lower-risk patients diagnosed without “laboratory confirmation.” And there is no basis for projecting that for infections in patients of similar risk profiles, death rates from coronavirus infections will be astronomically higher than for ‘flu patients.
Indeed, we can take comfort from the Wuhan data that 98.6% of patients whose condition prompted laboratory testing, survived. Further mutations may favor less lethal strains: viruses that kill their hosts don’t themselves survive, as the obstinate resilience of non-lethal cold viruses is believed to exemplify.
German chancellor Angela Merkel concedes that coronavirus infections are likely to become widespread, infecting 60-70 percent of the population, without much harm to most patients. But she warns, even with small proportions requiring intensive care in hospitals, rapid growth of infections could overwhelm the health care system.
The UK’s chief scientist however says the virus needs to infect 60% of the British population before it naturally peters out in summer. This is so that Britons develop the “herd immunity” that will protect against future outbreaks.
We can make reasonable estimates of hospital intensive care capacity. But, without large-scale testing – using tests of magically reliable accuracy – and diverting resources from other public health programs, we cannot know how many people already have infections: a seventy-something physician, who still goes to yoga class and rides the subway, believes “everyone living and working in Manhattan has been exposed, countless have already gotten sick and recovered” And, predictions of severe new cases – or the potential “next year” deaths from delayed herd immunity to dormant viruses – are also highly speculative.
Even if the fastest possible “flattening the curve” of infections is the right answer how best to do this is murky. If infections are already widespread, the scope – or need — for reducing their growth rates is limited. Similarly, we cannot predict what combination of ‘tough’ measures will actually slow progression. Might sending potentially infected college students back to their currently coronavirus free hometowns accelerate the spread of infections? What about declarations of national emergencies that drive hoarders into supermarkets where they don’t keep a safe social distance from people who haven’t washed their hands diligently? And, if we cannot reliably track infection rates we also cannot select and tweak interventions to maximize their effectiveness.
Worse, apocalyptic speculation and draconian measures that might or might not reduce overall long run mortality do immediate, obvious economic harm. Quarantines, travel bans, closing bars and public performances, and emptying college campuses jeopardizes the livelihoods of individuals and businesses who have no financial cushion to tide them over. Financial distress in turn can exact a severe toll on physical and mental health.
Experience with seasonal flus teaches the virtues of fortitude and dealing with things as they come. Death rates in the US attributed to flu infections swing widely and unpredictably – from 27,000 in good years to 70,000 in bad years. Intensive care units whose capacity is optimized for  normal demands, face acute stress in bad years. And, every new season may bring mutations as deadly as the catastrophic Spanish flu of 1917.  Lockdowns each winter could potentially save say 50,000 bad year deaths, reduce stress on intensive care units and forestall catastrophes. Fatal car accidents would also fall. Yet sensibly, we reject safety at all costs: we risk more deaths to live better lives. That’s a lesson worth remembering now.

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