IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/27532.html
   My bibliography  Save this paper

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

Author

Listed:
  • Anup Malani
  • Satej Soman
  • Sam Asher
  • Paul Novosad
  • Clement Imbert
  • Vaidehi Tandel
  • Anish Agarwal
  • Abdullah Alomar
  • Arnab Sarker
  • Devavrat Shah
  • Dennis Shen
  • Jonathan Gruber
  • Stuti Sachdeva
  • David Kaiser
  • Luis M.A. Bettencourt

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

  • Anup Malani & Satej Soman & Sam Asher & Paul Novosad & Clement Imbert & Vaidehi Tandel & Anish Agarwal & Abdullah Alomar & Arnab Sarker & Devavrat Shah & Dennis Shen & Jonathan Gruber & Stuti Sachdeva, 2020. "Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown," NBER Working Papers 27532, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27532
    Note: EH
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w27532.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anish Agarwal & Abdullah Alomar & Arnab Sarker & Devavrat Shah & Dennis Shen & Cindy Yang, 2020. "Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?," Papers 2005.00072, arXiv.org, revised May 2020.
    2. Luís M A Bettencourt & Ruy M Ribeiro, 2008. "Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases," PLOS ONE, Public Library of Science, vol. 3(5), pages 1-9, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christoph Zimmer & Reza Yaesoubi & Ted Cohen, 2017. "A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-21, January.
    2. William J. Luther, 2021. "Behavioral and Policy Responses to COVID-19: Evidence from Google Mobility Data on State- Level Stay-at-Home Orders," Journal of Private Enterprise, The Association of Private Enterprise Education, vol. 36(Fall 2021), pages 67-89.
    3. Kris V. Parag & Robin N. Thompson & Christl A. Donnelly, 2022. "Are epidemic growth rates more informative than reproduction numbers?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 5-15, November.
    4. Chad Cotti & Bryan Engelhardt & Joshua Foster & Erik Nesson & Paul Niekamp, 2021. "The relationship between in‐person voting and COVID‐19: Evidence from the Wisconsin primary," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 760-777, October.
    5. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    6. Victor W. Chu & Raymond K. Wong & Chi-Hung Chi & Wei Zhou & Ivan Ho, 2017. "The design of a cloud-based tracker platform based on system-of-systems service architecture," Information Systems Frontiers, Springer, vol. 19(6), pages 1283-1299, December.
    7. Kernel Prieto & M Victoria Chávez–Hernández & Jhoana P Romero–Leiton, 2022. "On mobility trends analysis of COVID–19 dissemination in Mexico City," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
    8. Schimit, P.H.T. & Monteiro, L.H.A., 2012. "On estimating the basic reproduction number in distinct stages of a contagious disease spreading," Ecological Modelling, Elsevier, vol. 240(C), pages 156-160.
    9. Ida Johnsson & M. Hashem Pesaran & Cynthia Fan Yang, 2023. "Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 across U.S. States and Selected Countries," CESifo Working Paper Series 10659, CESifo.
    10. Kernel Prieto, 2022. "Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.
    11. Sean Elliott & Christian Gourieroux, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Papers 2012.11542, arXiv.org.
    12. Mostafa Adimy & Julien Molina & Laurent Pujo-Menjouet & Grégoire Ranson & Jianhong Wu, 2022. "Forecasting the Effect of Pre-Exposure Prophylaxis (PrEP) on HIV Propagation with a System of Differential–Difference Equations with Delay," Mathematics, MDPI, vol. 10(21), pages 1-24, November.
    13. Dina Albassam & Mariam Nouh & Anette Hosoi, 2023. "The Effectiveness of Mobility Restrictions on Controlling the Spread of COVID-19 in a Resistant Population," IJERPH, MDPI, vol. 20(7), pages 1-14, March.
    14. Maeno, Yoshiharu, 2016. "Detecting a trend change in cross-border epidemic transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 73-81.
    15. Sean ELLIOTT & Christian GOURIEROUX, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Working Papers 2020-31, Center for Research in Economics and Statistics.
    16. José Ulises Márquez Urbina & Graciela González Farías & L Leticia Ramírez Ramírez & D Iván Rodríguez González, 2022. "A multi-source global-local model for epidemic management," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-26, January.
    17. Anup Malani & Satej Soman & Sabareesh Ramachandran & Alice Chen & Darius N. Lakdawalla, 2022. "Vaccine Allocation Priorities Using Disease Surveillance and Economic Data," NBER Working Papers 29682, National Bureau of Economic Research, Inc.
    18. Kyle S Hickmann & Geoffrey Fairchild & Reid Priedhorsky & Nicholas Generous & James M Hyman & Alina Deshpande & Sara Y Del Valle, 2015. "Forecasting the 2013–2014 Influenza Season Using Wikipedia," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-29, May.
    19. Park, Dojoon & Kang, Yong Joo & Eom, Young Ho, 2024. "Asset pricing tests for pandemic risk," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1314-1334.
    20. De Simone, Andrea & Piangerelli, Marco, 2020. "A Bayesian approach for monitoring epidemics in presence of undetected cases," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    More about this item

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:27532. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.