Adaptive Control of Covid-19 Outbreaks in India: Local, Gradual, and Trigger-Based Exit Paths from Lockdown

30 Pages Posted: 24 Jul 2020 Last revised: 24 Sep 2022

See all articles by Anup Malani

Anup Malani

University of Chicago - Law School; National Bureau of Economic Research (NBER); University of Chicago Pritzker School of Medicine; Resources for the Future

Satej Soman

University of Chicago

Sam Asher

World Bank Development Research Group (DECRG)

Paul Michael Novosad

Dartmouth College - Department of Economics

Clément Imbert

University of Warwick

Vaidehi Tandel

University of Manchester

Anish Agarwal

Massachusetts Institute of Technology (MIT)

Abdullah Alomar

King Abdulaziz City for Science and Technology (KACST)

Arnab Sarker

Massachusetts Institute of Technology (MIT)

Devavrat Shah

Massachusetts Institute of Technology

Dennis Shen

Massachusetts Institute of Technology (MIT)

Jonathan Gruber

Massachusetts Institute of Technology (MIT) - Department of Economics

Stuti Sachdeva

Massachusetts Institute of Technology (MIT)

David Kaiser

Massachusetts Institute of Technology (MIT)

Luis Bettencourt

University of Chicago - Mansueto Institute for Urban Innovation

Date Written: July 2020

Abstract

Managing the outbreak of COVID-19 in India constitutes an unprecedented health emergency in one of the largest and most diverse nations in the world. On May 4, 2020, India started the process of releasing its population from a national lockdown during which extreme social distancing was implemented. We describe and simulate an adaptive control approach to exit this situation, while maintaining the epidemic under control. Adaptive control is a flexible counter-cyclical policy approach, whereby different areas release from lockdown in potentially different gradual ways, dependent on the local progression of the dis- ease. Because of these features, adaptive control requires the ability to decrease or increase social distancing in response to observed and projected dynamics of the disease outbreak. We show via simulation of a stochastic Susceptible-Infected-Recovered (SIR) model and of a synthetic intervention (SI) model that adaptive control performs at least as well as immediate and full release from lockdown starting May 4 and as full release from lockdown after a month (i.e., after May 31). The key insight is that adaptive response provides the option to increase or decrease socioeconomic activity depending on how it affects disease progression and this freedom allows it to do at least as well as most other policy alternatives. We also discuss the central challenge to any nuanced release policy, including adaptive control, specifically learning how specific policies translate into changes in contact rates and thus COVID-19's reproductive rate in real time.

Suggested Citation

Malani, Anup and Soman, Satej and Asher, Sam and Novosad, Paul Michael and Imbert, Clément and Tandel, Vaidehi and Agarwal, Anish and Alomar, Abdullah and Sarker, Arnab and Shah, Devavrat and Shen, Dennis and Gruber, Jonathan and Sachdeva, Stuti and Kaiser, David and Bettencourt, Luis, Adaptive Control of Covid-19 Outbreaks in India: Local, Gradual, and Trigger-Based Exit Paths from Lockdown (July 2020). NBER Working Paper No. w27532, Available at SSRN: https://ssrn.com/abstract=3658826

Anup Malani (Contact Author)

University of Chicago - Law School ( email )

1111 E. 60th St.
Chicago, IL 60637
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HOME PAGE: http://www.law.uchicago.edu/faculty/malani/

National Bureau of Economic Research (NBER)

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University of Chicago Pritzker School of Medicine

Chicago, IL 60637
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Resources for the Future

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Satej Soman

University of Chicago

Sam Asher

World Bank Development Research Group (DECRG) ( email )

1818 H. Street, N.W.
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Washington, DC 20433
United States

HOME PAGE: http://samuelasher.com

Paul Michael Novosad

Dartmouth College - Department of Economics ( email )

Department of Sociology
Hanover, NH 03755
United States

Clément Imbert

University of Warwick

Gibbet Hill Rd.
Coventry, CV4 8UW
United Kingdom

Vaidehi Tandel

University of Manchester ( email )

Oxford Road
Manchester, N/A M13 9PL
United Kingdom

Anish Agarwal

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
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Abdullah Alomar

King Abdulaziz City for Science and Technology (KACST)

Arnab Sarker

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Devavrat Shah

Massachusetts Institute of Technology ( email )

Cambridge, MA
United States

HOME PAGE: http://www.mit.edu/~devavrat/

Dennis Shen

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
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Jonathan Gruber

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

50 Memorial Drive
E52-391
Cambridge, MA 02142
United States

Stuti Sachdeva

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

David Kaiser

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Luis Bettencourt

University of Chicago - Mansueto Institute for Urban Innovation ( email )

5735 S Ellis Avenue
Chicago, IL 60637
United States

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