It may be useful to review how we arrived in our present situation regarding the coronavirus. The decision to institute shutdown orders and stringent social distancing was based on a model constructed before there was significant experience with COVID-19. That model or rather those classes of models were revised several times. It is reasonable to ask, now with more than two months experience with COVID-9, whether those models are useful, or if they even were.
As mentioned previously, policy decisions are justified by reference to some hypothetical number of lives saved. This is a pointless exercise for several reasons. First, it is not a reference to lives saved exactly, it is a reference to lives that may be saved from a particular cause of death. It provides no guidance for lives that may be lost from other causes, including those that may be affected by interventions meant to slow the spread of coronavirus. Secondly, it bears remembering that the rationale behind "flattening the curve" was not to prevent people dying of coronavirus, it was meant to minimize the number of people who may die from lack of access to healthcare whether or not they have coronavirus. Thirdly the specific interventions imposed were based on striking estimates of deaths, 3 million in the United States and 500,000 in the United Kingdom if nothing were done. Certainly, if that estimate had been 10,000 deaths in the United States, or fewer than the number of deaths attributed to the 2009 H1N1 pandemic, we would have expected the response, and suite of interventions to be similar to those of eleven years ago. The key realization about this, is that it is contrary to the notion that any burden is worth it if it saves demonstrable number of lives. In the extreme, the fatuous notion that "if it saves one life" is contradicted by the fact that 10,000 lives lost would result in a different level of response than would the prospect of 3 million. For some reason, 10,000 deaths may be accepted as part of the human condition, but one death cannot be accepted at all as a point of public discourse.
At some point it has to be made clear whether decision-makers are continuing to make decisions based on models or whether they are doing so based on the three months worth of data available to them. It should be a matter of principled decision-making and competent governance to describe the criteria on which the decisions are based. can life begin to return to normal when the number of daily new cases declines to a pre-determined value, or percentage of the peak value? Is the consideration the number of daily deaths? Number of ventilators? Hospital capacity? Number of tests?
Data are collected for various purposes and those purposes affect how the data are collected and interpreted. There may be one purpose that prioritizes accuracy over inclusiveness, and therefore would reject death counts that included those that were never tested for coronavirus. Another interest may value inclusiveness over accuracy and be more accommodating of "presumed" cases. Some data such as lactate dehydrogenase values may be dismissed by physicians caring for patients as not likely to affect management, but these same data may be essential to constructing databases that provide more useful models of what factors determine disease severity and prognosis. Other data may be useful for risk stratification or resource allocation, or to justify reimbursement requests. In short, the value of data depends on the context and purpose for which it is collected. Data that is essential to some endeavors may be next to useless for others. IF the data we have available forms the basis of policy decisions, this fact must be considered. Are the data appropriate to policy under consideration, or are they skewed by other interests?
An analogous issue pertains to "experts." These people, who may be quite accomplished in a particular field, but who are not sympathetic to wider policy needs, may advocate for data that are relevant to their own fields and curiosities. Thus, data regarding the effectiveness of a particular intervention, such as convalescent plasma, may be useless if it is not collected in the context of patient randomization or placebo control, even though these factors may be irrelevant to family members who have a relative in tenuous condition on a ventilator.
Other policy decisions defy easy measurement regardless of the procedures by which data are collected.. It is unlikely that there will ever be a serious estimate of how many lives were saved, if any, by the distinction between essential and non-essential items that could be purchased from a store that sells both. Of course, it would be possible, and perhaps irresistible to construct a model that would provide an estimate, but when we get to that point we may as well begin meta-modeling and ask how much harm or benefit resulted from relying on models in the first place.
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