Modeling-Informed Policy, Policy Evaluated by Modeling

The coronavirus disease 2019 (COVID-19) pandemic that has had the world in its grip since the beginning of 2020 has resulted in an unprecedented level of public interest and media attention focusing on the field of mathematical epidemiology. Ever since the disease came to worldwide attention, numerous models with varying levels of sophistication have been proposed; many of these have tried to predict the course of the disease over different timescales, ranging from attempting to project the number of active cases over successive days and weeks to attempts at forecasting the date(s) on which subsequent “waves” of the pandemic will purportedly emerge. Other models have examined the efficacy of the various policy measures that have been adopted (including the unparalleled use of “lockdowns,” i.e., extremely stringent restrictions on travel, social interaction, and access to public spaces) by countries around the world in an attempt to contain and combat the disease. This multiplicity of models may have given the impression of an apparent overabundance of distinct mathematical approaches to investigate how pandemics evolve over time. More importantly, some of the more extravagant claims made by a few modeling groups about their ability to predict future outcomes, which went hand in hand with the occasional abuse of models by agencies with vested interests, have led to bewilderment in many quarters about the true capabilities and utility of mathematical modeling. Here, we provide a brief guide to the epidemiological modeling enterprise, focusing on how it has emerged as a tool for informed public health policy-making and has, in turn, influenced the design of interventions aimed at preventing disease outbreaks from turning into raging epidemics. We show that the diversity of models is somewhat illusory, as the bulk of them are rooted in the compartmental modeling framework that we describe here. While its basic structure may appear to be a highly idealized description of the processes at work, we show that features that provide more realism, such as the community organization of populations or strategic decision-making by individuals, can be incorporated into such models to make them behave in accordance with empirical observations. We conclude with the argument that the true value of models lies in their ability to test in silico the consequences of different policy choices in the course of an epidemic, a much superior alternative to trial-and-error approaches that are highly costly in terms of both lives and socioeconomic disruption.