Sequential Lifting of COVID-19 Interventions with Population Heterogeneity

Rampini, Adriano A.

doi:10.2139/ssrn.3579183

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Sequential Lifting of COVID-19 Interventions with Population Heterogeneity

20 Pages Posted: 22 Apr 2020 Last revised: 23 Apr 2020

See all articles by Adriano A. Rampini

Adriano A. Rampini

Duke University; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR)

There are 2 versions of this paper

Date Written: April 17, 2020

Abstract

This paper analyzes a sequential approach to lifting interventions in the COVID-19 pandemic taking heterogeneity in the population into account. The population is heterogeneous in terms of the consequences of infection (need for hospitalization and critical care, and mortality) and in terms of labor force participation. Splitting the population in two groups by age, a less affected younger group that is more likely to work, and a more affected older group less likely to work, and lifting interventions sequentially (for the younger group first and the older group later on) can substantially reduce mortality, demands on the health care system, and the economic cost of interventions.

Keywords: COVID-19; pandemic; epidemiological model

JEL Classification: H10, E10, I18, H12, I10

Suggested Citation: Suggested Citation

Rampini, Adriano A., Sequential Lifting of COVID-19 Interventions with Population Heterogeneity (April 17, 2020). Available at SSRN: https://ssrn.com/abstract=3579183 or http://dx.doi.org/10.2139/ssrn.3579183