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A Multi-Risk SIR Model with Optimally Targeted Lockdown

Author

Listed:
  • Daron Acemoglu

    (Institute for Fiscal Studies)

  • Victor Chernozhukov

    (Institute for Fiscal Studies and MIT)

  • Ivàn Werning

    (Institute for Fiscal Studies and MIT)

  • Michael D. Whinston

    (Institute for Fiscal Studies)

Abstract

We study targeted lockdowns in a multi-group SIR model where infection, hospitalization and fatality rates vary between groups—in particular between the 'young', 'the middle-aged' and the 'old'. Our model enables a tractable quantitative analysis of optimal policy. For baseline parameter values for the COVID-19 pandemic applied to the US, we find 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. Intuitively, a strict and long lockdown for the most vulnerable group both reduces infections and enables less strict lockdowns for the lower-risk groups. We also study the impacts of group distancing, testing and contract tracing, the matching technology and the expected arrival time of a vaccine on optimal policies. Overall, targeted policies that are combined with measures that reduce interactions between groups and increase testing and isolation of the infected can minimize both economic losses and deaths in our model. Download Updated Version

Suggested Citation

  • Daron Acemoglu & Victor Chernozhukov & Ivàn Werning & Michael D. Whinston, 2020. "A Multi-Risk SIR Model with Optimally Targeted Lockdown," CeMMAP working papers CWP14/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:14/20
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    References listed on IDEAS

    as
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