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Decision Making with Regard to Antiviral Intervention during an Influenza Pandemic

Author

Listed:
  • Eunha Shim

    (Department of Epidemiology & Public Health, Yale School of Public Health, New Haven, CT, eunha.shim@yale.edu)

  • Gretchen B. Chapman

    (Department of Psychology, Rutgers University, Piscataway, NJ)

  • Alison P. Galvani

    (Department of Epidemiology & Public Health, Yale School of Public Health, New Haven, CT)

Abstract

Background. Antiviral coverage is defined by the proportion of the population that takes antiviral prophylaxis or treatment. High coverage of an antiviral drug has epidemiological and evolutionary repercussions. Antivirals select for drug resistance within the population, and individuals may experience adverse effects. To determine optimal antiviral coverage in the context of an influenza outbreak, we compared 2 perspectives: 1) the individual level (the Nash perspective), and 2) the population level (utilitarian perspective). Methods. We developed an epidemiological game-theoretic model of an influenza pandemic. The data sources were published literature and a national survey. The target population was the US population. The time horizon was 6 months. The perspective was individuals and the population overall. The interventions were antiviral prophylaxis and treatment. The outcome measures were the optimal coverage of antivirals in an influenza pandemic. Results. At current antiviral pricing, the optimal Nash strategy is 0% coverage for prophylaxis and 30% coverage for treatment, whereas the optimal utilitarian strategy is 19% coverage for prophylaxis and 100% coverage for treatment. Subsidizing prophylaxis by $440 and treatment by $85 would bring the Nash and utilitarian strategies into alignment. For both prophylaxis and treatment, the optimal antiviral coverage decreases as pricing of antivirals increases. Our study does not incorporate the possibility of an effective vaccine and lacks probabilistic sensitivity analysis. Our survey also does not completely represent the US population. Because our model assumes a homogeneous population and homogeneous antiviral pricing, it does not incorporate heterogeneity of preference. Conclusions. The optimal antiviral coverage from the population perspective and individual perspectives differs widely for both prophylaxis and treatment strategies. Optimal population and individual strategies for prophylaxis and treatment might be aligned through subsidization.

Suggested Citation

  • Eunha Shim & Gretchen B. Chapman & Alison P. Galvani, 2010. "Decision Making with Regard to Antiviral Intervention during an Influenza Pandemic," Medical Decision Making, , vol. 30(4), pages 64-81, July.
    • Handle: RePEc:sae:medema:v:30:y:2010:i:4:p:e64-e81
    DOI: 10.1177/0272989X10374112
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    References listed on IDEAS

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    Cited by:

    1. Dan Yamin & Arieh Gavious, 2013. "Incentives' Effect in Influenza Vaccination Policy," Management Science, INFORMS, vol. 59(12), pages 2667-2686, December.
    2. Ding, Hong & Xu, Jia-Hao & Wang, Zhen & Ren, Yi-Zhi & Cui, Guang-Hai, 2018. "Subsidy strategy based on history information can stimulate voluntary vaccination behaviors on seasonal diseases," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 390-399.
    3. Travis C Porco & Daozhou Gao & James C Scott & Eunha Shim & Wayne T Enanoria & Alison P Galvani & Thomas M Lietman, 2012. "When Does Overuse of Antibiotics Become a Tragedy of the Commons?," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-12, December.

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