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Optimal Targeted Lockdowns in a Multi-Group SIR Model

Michael Whinston, Daron Acemoglu, Victor Chernozhukov, and Iván Werning
May 2020
Read the paper here

The COVID-19 pandemic has caused global upheaval that may endure for months—or longer. Many predictions of the effects of the pandemic have been dire. Partner Michael Whinston and his co-authors began thinking about ways to improve the set of grim options put forth for society in those early reports. They studied targeted lockdowns in a multi-group SIR (susceptible, infected, and recovered) model where infection, hospitalization, and fatality rates vary between groups, which also allows a tractable quantitative analysis of optimal policy.

For baseline parameter values for the COVID-19 pandemic applied to the United States, their research finds that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies, and most of the gains can be realized by having stricter lockdown policies on the oldest group.  In "Optimal Targeted Lockdowns in a Multi-Group SIR Model," the authors also examine the impacts of group distancing, testing, and contract tracing, and the expected arrival time of a vaccine on optimal policies. They conclude that, overall, targeted policies that are combined with measures to reduce interactions between groups and increase testing and isolation of the infected can minimize both economic losses and deaths.

On May 22, Dr. Whinston presented the research at the London School of Economics and Political Science in a webcast hosted by the Department of Economics. He discussed what economists can offer in this context—recognizing the costs of the lockdown, examining tradeoffs among the policy options, and examining one policy to see its possible effects. A recording of Dr. Whinston’s presentation is available to view.

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