Thinking in a Pandemic by Boston Review

Author:Boston Review
Language: eng
Format: epub
Publisher: Verso

Where does this clash of sensibilities leave us? In my own work, I have modeled prediction in evidence-based medicine as a chain of inferences. Each individual inference is a link forged from assumptions in need of evidence; the chain is broken if any assumption breaks down. In their book Evidence-Based Policy (2012), philosopher of science Nancy Cartwright and economist Jeremy Hardie represent predictions about the effectiveness of a policy using a pyramid. The top level, the hypothesis that the policy will work in some local context, rests on several assumptions, which rest on further assumptions, and so on. Without evidence for the assumptions, the entire structure falls.

Either picture is a good metaphor for the relationship between evidence and models. Evidence is needed to support modeling assumptions to generate predictions that are more precise and accurate. Evidence is also needed to rule out alternative assumptions, and thus alternative predictions. Models represent a multiverse of hypothetical futures. Evidence helps us predict which future will materialize directly by filling in its contours, and indirectly by scratching out other hypothetical worlds.

The need for evidence and modeling will not dissolve when the dust settles in our future world. In evaluating the choices we made and the effectiveness of our policies, we will need to predict what would have happened otherwise. Such a judgment involves comparing worlds: the actual world that materialized and some hypothetical world that did not. How many COVID-19 deaths did our social distancing measures prevent? We can estimate the number of COVID-19 deaths in our actual socially distanced world by counting, but to predict the number of COVID-19 deaths in an unchosen world without social distancing, we will need to dust off our models and evidence.

Just as we should embrace both models and evidence, we should welcome both of epidemiology’s competing philosophies. This may sound like a boring conclusion, but in the coronavirus pandemic there is no glory, and there are no winners. Cooperation in society should be matched by cooperation across disciplinary divides. The normal process of scientific scrutiny and peer review has given way to a fast track from research offices to media headlines and policy panels. Yet the need for criticism from diverse minds remains.

I mentioned that the discovery that smoking causes lung cancer was a discipline-defining achievement for public health epidemiology, while the British Medical Research Council’s streptomycin trial was a formative episode in the history of clinical epidemiology. Epidemiologist Austin Bradford Hill played a role in both scientific achievements. He promoted the clinical trial in medicine and also provided nine criteria (“Hill’s Viewpoints”) still used in public health epidemiology for making causal inferences from a diversity of data.

Like Hill, epidemiology should be of two minds. It must combine theory with evidence and make use of diverse data while demanding data of increasingly higher quality. It must be liberal in its reasoning but conservative in its conclusions, pragmatic in its decision making while remaining skeptical of its own science. It must be split-brained, acting with one hand while collecting more information with the other.

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