Models for government intervention during a pandemic

Abstract

While intervention policies such as social distancing rules, lockdowns, and curfews may save lives during a pandemic, they impose substantial direct and indirect costs on societies. In this paper, we provide a mathematical model to assist governmental policymakers in managing the lost lives during a pandemic through controlling intervention levels. Our model is non-convex in decision variables, and we develop two heuristics to obtain fast and high-quality solutions. Our results indicate that when anticipated economic consequences are higher, healthcare overcapacity will emerge. When the projected economic costs of the pandemic are large and the illness severity is low, however, a no-intervention strategy may be preferable. As the severity of the infection rises, the cost of intervention climbs accordingly. The death toll also increases with the severity of both the economic consequences of interventions and the infection rate of the disease. Our models suggest earlier mitigation strategies that typically start before the saturation of the healthcare system when disease severity is high.