Optimizing Disease Containment Measures during a Pandemic

Abstract

Throughout the current COVID-19 pandemic, governments have implemented a variety of containment measures, ranging from hoping for herd immunity (which is essentially no containment) to mandating complete lockdown. On one hand, containment measures limit the spread of the disease, controlling the load on the healthcare system and reducing lives lost. On the other, such measures drag down economic activity, leading to lost jobs, economic stall, and societal disturbances, such as protests, civil disobedience, and increases in domestic violence. Hence, determining the right set of containment measures is a key social, economic, and political decision for policymakers.

In this paper, we provide a model for dynamically optimizing the level of disease containment measures over the course of a pandemic. We determine the timing and level of containment measures to minimize the impact of a pandemic on economic activity and lives lost, subject to healthcare capacity and stochastic disease evolution dynamics. Based on practical evidence, we examine two common classes of containment policies – dynamic and static – and we find that the value of dynamic policies over static policies increases with lower healthcare capacity, higher disease infection rate, and longer disease recovery period. Our work reveals a fundamental relationship between the structure of Pareto-efficient containment measures (in terms of lives lost and economic activity) and key disease and economic parameters such as disease infection rate, recovery rate, and healthcare capacity. We also examine the impact possibilities of virus mutation and vaccination on the containment policies.

Note: Funding Statement: There is no funding associated with this work.

Declaration of Interests: There are no competing interests.

Keywords: pandemic, COVID-19, optimization, public policy, containment measures