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: HC HE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w27532.pdf
    Download Restriction: Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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.
    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.

      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.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/nberrus.html .

      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: (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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.