Working Paper: NBER ID: w27063
Authors: Adriano A. Rampini
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 Codes: E32; E44; E65; H12; I10; I18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Lifting interventions for the less affected younger group first (C92) | reduced mortality (I14) |
| Lifting interventions for the less affected younger group first (C92) | reduced economic costs (D61) |
| Sequential lifting of interventions (C36) | reduces peak hospitalization demand (I11) |
| Sequential lifting of interventions (C36) | reduces critical care demand (I11) |
| Recovery of a significant portion of the younger group (J26) | reduces infectiousness (I12) |
| Sequential approach (C69) | achieves herd immunity with lower fraction of population infected (C92) |