Using Quantitative Models to Guide Policy Amid COVID-19 Uncertainty

Policymakers look to forecasts or projections about the future evolution of contagion and subsequent fatalities to guide their policy choices. These can be best guesses or warnings about how bad things could become. These considerations factor into their decision making in at least informal ways. Epidemiologists no doubt have important insights that we all look to digest. Economists and other social scientists are quick to consider ways by which they can draw upon their current stock of knowledge to incorporate endogenous responses of individuals and businesses to various policy alternatives. Quantitative predictions of disease transmission under alternative policies and the resulting social behaviors, however, bring special challenges. The reason is that models require specific assumptions and ingredients that govern the dynamic evolution and consequences of alternative forms of social and economic interactions. Subjective judgements are unavoidable. There are unknown parameters to calibrate in the face of limited data. These challenges are pervasive in quantitative modeling that aims to support policy. The unique challenges of the COVID-19 global situation are what draws our attention as we witness and participate in this harrowing episode.