Assessing Targeted Containment Policies to Fight COVID-19

IMF Working Papers, December 11, 2020

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The paper assesses targeted containment as an alternative to non-discriminatory (blanket) lockdowns in response to COVID-19 outbreaks.
It develops a SIR-macro model with heterogeneous agents by mortality rates and contact rates, where the government optimally bans people from working.
Core tradeoff: targeted containment reduces deaths relative to blanket policies but leads to a larger and longer recession because containment applies to a larger fraction of people, is held for longer, and delays herd immunity.
Increased interactions between low- and high-risk individuals reduce the effectiveness of targeted containment.

Model type: SIR-macro model incorporating heterogeneous agents in mortality rates and contact rates.
Government policy instrument: optimal bans on working for portions of the population.
Behavioral feature considered: voluntary social distancing (mentioned as a keyword).

Under a targeted policy, the optimal containment:
- Reaches a larger portion of the population than under a blanket policy.
- Is held in place for longer than under a blanket policy.
Health outcome:
- A targeted approach results in a smaller death count compared to a blanket policy.
Economic outcome:
- The recession is larger under a targeted approach than under a blanket policy.
- Reasons: containment applies to a larger fraction of people, remains in place longer, and herd immunity is achieved later.
Interaction effects:
- Increased interactions between low- and high-risk individuals effectively reduce the benefits of a targeted approach.

Targeted containment can reduce mortality but at the cost of deeper and more prolonged economic contraction.
Policymakers should consider the degree of interaction between risk groups when evaluating targeted containment, as high interaction undermines benefits.
Optimal policy design must balance mortality reduction against larger recessionary effects and delayed herd immunity.