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Dynamic Health Policies for Controlling the Spread of Emerging Infections: Influenza as an Example

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  • Reza Yaesoubi
  • Ted Cohen

Abstract

The recent appearance and spread of novel infectious pathogens provide motivation for using models as tools to guide public health decision-making. Here we describe a modeling approach for developing dynamic health policies that allow for adaptive decision-making as new data become available during an epidemic. In contrast to static health policies which have generally been selected by comparing the performance of a limited number of pre-determined sequences of interventions within simulation or mathematical models, dynamic health policies produce “real-time” recommendations for the choice of the best current intervention based on the observable state of the epidemic. Using cumulative real-time data for disease spread coupled with current information about resource availability, these policies provide recommendations for interventions that optimally utilize available resources to preserve the overall health of the population. We illustrate the design and implementation of a dynamic health policy for the control of a novel strain of influenza, where we assume that two types of intervention may be available during the epidemic: (1) vaccines and antiviral drugs, and (2) transmission reducing measures, such as social distancing or mask use, that may be turned “on” or “off” repeatedly during the course of epidemic. In this example, the optimal dynamic health policy maximizes the overall population's health during the epidemic by specifying at any point of time, based on observable conditions, (1) the number of individuals to vaccinate if vaccines are available, and (2) whether the transmission-reducing intervention should be either employed or removed.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0024043
    DOI: 10.1371/journal.pone.0024043
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Maria Laura Silva & Lionel Perrier & Jean Marie Cohen & Anne Mosnier & John Paget & Hans Martin Spath, 2013. "Literature review of the decision-making determinants related to the influenza vaccination policy," Working Papers 1317, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    2. Margherita, Alessandro & Elia, Gianluca & Klein, Mark, 2021. "Managing the COVID-19 emergency: A coordination framework to enhance response practices and actions," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Biswas, Debajyoti & Alfandari, Laurent, 2022. "Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1372-1391.
    4. Laura Matrajt & M Elizabeth Halloran & Ira M Longini Jr, 2013. "Optimal Vaccine Allocation for the Early Mitigation of Pandemic Influenza," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-15, March.

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