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A Statistical Framework for the Adaptive Management of Epidemiological Interventions

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  • Daniel Merl
  • Leah R Johnson
  • Robert B Gramacy
  • Marc Mangel

Abstract

Background: Epidemiological interventions aim to control the spread of infectious disease through various mechanisms, each carrying a different associated cost. Methodology: We describe a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic while simultaneously propagating uncertainty regarding the underlying disease model parameters through to the decision process. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. Conclusions: Using simulation studies based on a classic influenza outbreak, we demonstrate the advantages of adaptive interventions over non-adaptive ones, in terms of cost and resource efficiency, and robustness to model misspecification.

Suggested Citation

  • Daniel Merl & Leah R Johnson & Robert B Gramacy & Marc Mangel, 2009. "A Statistical Framework for the Adaptive Management of Epidemiological Interventions," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-9, June.
    • Handle: RePEc:plo:pone00:0005807
    DOI: 10.1371/journal.pone.0005807
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    References listed on IDEAS

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    1. Neil M. Ferguson & Matt J. Keeling & W. John Edmunds & Raymond Gani & Bryan T. Grenfell & Roy M. Anderson & Steve Leach, 2003. "Planning for smallpox outbreaks," Nature, Nature, vol. 425(6959), pages 681-685, October.
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    Cited by:

    1. Reza Yaesoubi & Ted Cohen, 2011. "Dynamic Health Policies for Controlling the Spread of Emerging Infections: Influenza as an Example," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
    2. Yaesoubi, Reza & Cohen, Ted, 2011. "Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies," European Journal of Operational Research, Elsevier, vol. 215(3), pages 679-687, December.
    3. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2018. "Control fast or control smart: When should invading pathogens be controlled?," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-21, February.
    4. Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
    5. Martial L Ndeffo Mbah & Christopher A Gilligan, 2011. "Resource Allocation for Epidemic Control in Metapopulations," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-10, September.
    6. repec:jss:jstsof:36:i06 is not listed on IDEAS
    7. Anne-France Viet & Stéphane Krebs & Olivier Rat-Aspert & Laurent Jeanpierre & Catherine Belloc & Pauline Ezanno, 2018. "A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-20, June.

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