History and Philosophy of the Life Sciences                              volume  43, Article number: 81  (2021 )

Imagination and remembrance: what role should historical epidemiology play in a world bewitched by mathematical modelling of COVID-19 and other epidemics?

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2021-06-14 11:00:03

History and Philosophy of the Life Sciences volume  43, Article number: 81 (2021 ) Cite this article

Although every emerging infectious disease occurs in a unique context, the behaviour of previous pandemics offers an insight into the medium- and long-term outcomes of the current threat. Where an informative historical analogue exists, epidemiologists and policymakers should consider how the insights of the past can inform current forecasts and responses.

The emergence of COVID-19 has seen an explosion of epidemiological models seeking to characterise and forecast the course of the pandemic. The outputs of these models have influenced policy decisions around the world despite extremely uneven forecasting performance of similar models of other recent emerging infectious diseases. Instead, one might look to data from past pandemics to inform current risk assessments. Some view such analogies to events of the past as unreliable, raising the reductionist truism that every combination of disease and context is unique (Peckham 2020). However, both epidemiological modelling of future scenarios and analyses of historical data are liable to errors of inputs, assumptions and interpretations; in this paper we argue that both techniques should be considered “wrong, but useful” (Christley et al. 2013) and that greater awareness of historical data may improve pandemic preparedness and responses.

The construction of an epidemiological model incorporates structural assumptions about the system under study and requires the assembly of input data describing the specific context and disease. Complex biological systems resist this simple parametrisation and models of these systems necessarily involve simplifications whose impact on the predictive skill of the model are difficult to quantify. In the early phase of a new epidemic, the input conditions for these models are gleaned from imperfect observations affected by, for example, ascertainment, time-based, and reporting biases that undermine both their accuracy and precision. The sensitivity of these models to their input conditions, and the appropriateness and stability of their structural assumptions, lends substantial uncertainty to any predictions made; their extrapolation beyond the initial time, place, or pathogen is even less secure.

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