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The COronaVIrus Disease 2019 (COVID-19) pandemic that has had the world inits grip from the beginning of 2020, has resulted in an unprecedented level ofpublic interest and media attention on the field of mathematical epidemiology.Ever since the disease came to worldwide attention, numerous models withvarying levels of sophistication have been proposed; many of these have triedto predict the course of the disease over different time-scales. Other modelshave examined the efficacy of various policy measures that have been adopted(including the unparalleled use of "lockdowns") to contain and combat thedisease. This multiplicity of models may have led to bewilderment in manyquarters about the true capabilities and utility of mathematical modeling. Herewe provide a brief guide to epidemiological modeling, focusing on how it hasemerged as a tool for informed public-health policy-making and has in turn,influenced the design of interventions aimed at preventing disease outbreaksfrom turning into raging epidemics. We show that the diversity of models issomewhat illusory, as the bulk of them are rooted in the compartmental modelingframework that we describe here. While their basic structure may appear to be ahighly idealized description of the processes at work, we show that featuresthat provide more realism, such as the community organization of populations orstrategic decision-making by individuals, can be incorporated into such models.We conclude with the argument that the true value of models lies in theirability to test in silico the consequences of different policy choices in thecourse of an epidemic, a much superior alternative to trial-and-errorapproaches that are highly costly in terms of both lives and socio-economicdisruption.

Modeling-informed policy, policy evaluated by modeling: Evolution of mathematical epidemiology in the context of society and economy