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Swine influenza and vaccines: an alternative approach for decision making about pandemic prevention

Author

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  • Marcello Basili
  • Silvia Ferrini
  • Emanuele Montomoli

Abstract

Background: During the global pandemic of N1H1 (2009) influenza, many Governments signed contracts with vaccine producers for a universal influenza immunization program and bought hundreds of millions of vaccines doses. We argue that, as Health Ministers assumed the occurrence of the worst possible scenario (generalized pandemic influenza) and followed the strong version of the Precautionary Principle, they undervalued the possibility of mild or weak pandemic wave.Methodology: An alternative decision rule, based on the non-extensive entropy principle, is introduced and a different Precautionary Principle characterization is applied. This approach values extreme negative results (catastrophic events) in a different way than ordinary results (more plausible and mild events), and introduces less pessimistic forecasts in the case of uncertain influenza pandemic outbreaks. A simplified application is presented through an example based on seasonal data of morbidity and severity among Italian children influenza-like illness for the period 2003-2010.Principal Findings: Compared to a pessimistic forecast by experts, who predict an average attack rate of 15% for the next pandemic influenza, we demonstrate that, using the non-extensive maximum entropy principle, a less pessimistic outcome is predicted with a 20% savings in public funding for vaccines doses.Conclusions: The need for an effective influenza pandemic prevention program, coupled with an efficient use of public funding, calls for a rethinking of the Precautionary Principle. The non-extensive maximum entropy principle, which incorporates vague and incomplete information available to decision makers, produces a more coherent forecast of possible influenza pandemic and a conservative spending in public funding.

Suggested Citation

  • Marcello Basili & Silvia Ferrini & Emanuele Montomoli, 2012. "Swine influenza and vaccines: an alternative approach for decision making about pandemic prevention," Department of Economics University of Siena 647, Department of Economics, University of Siena.
  • Handle: RePEc:usi:wpaper:647
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    References listed on IDEAS

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    1. José Lara Resende & George Wu, 2010. "Competence effects for choices involving gains and losses," Journal of Risk and Uncertainty, Springer, vol. 40(2), pages 109-132, April.
    2. Marcello Basili & Alain Chateauneuf, 2011. "Extreme events and entropy: A multiple quantile utility model," Post-Print hal-00685405, HAL.
    3. In Jae Myung & Sridhar Ramamoorti & Andrew D. Bailey, Jr., 1996. "Maximum Entropy Aggregation of Expert Predictions," Management Science, INFORMS, vol. 42(10), pages 1420-1436, October.
    4. Arthur E. Attema & Anna K. Lugnér & Talitha L. Feenstra, 2010. "Investment in antiviral drugs: a real options approach," Health Economics, John Wiley & Sons, Ltd., vol. 19(10), pages 1240-1254, October.
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    More about this item

    JEL classification:

    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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