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Optimal Vaccine Allocation for the Early Mitigation of Pandemic Influenza

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  • Laura Matrajt
  • M Elizabeth Halloran
  • Ira M Longini Jr

Abstract

With new cases of avian influenza H5N1 (H5N1AV) arising frequently, the threat of a new influenza pandemic remains a challenge for public health. Several vaccines have been developed specifically targeting H5N1AV, but their production is limited and only a few million doses are readily available. Because there is an important time lag between the emergence of new pandemic strain and the development and distribution of a vaccine, shortage of vaccine is very likely at the beginning of a pandemic. We coupled a mathematical model with a genetic algorithm to optimally and dynamically distribute vaccine in a network of cities, connected by the airline transportation network. By minimizing the illness attack rate (i.e., the percentage of people in the population who become infected and ill), we focus on optimizing vaccine allocation in a network of 16 cities in Southeast Asia when only a few million doses are available. In our base case, we assume the vaccine is well-matched and vaccination occurs 5 to 10 days after the beginning of the epidemic. The effectiveness of all the vaccination strategies drops off as the timing is delayed or the vaccine is less well-matched. Under the best assumptions, optimal vaccination strategies substantially reduced the illness attack rate, with a maximal reduction in the attack rate of 85%. Furthermore, our results suggest that cooperative strategies where the resources are optimally distributed among the cities perform much better than the strategies where the vaccine is equally distributed among the network, yielding an illness attack rate 17% lower. We show that it is possible to significantly mitigate a more global epidemic with limited quantities of vaccine, provided that the vaccination campaign is extremely fast and it occurs within the first weeks of transmission. Author Summary: In the past, the emergence of new strains of influenza has been sometimes responsible for large and deadly pandemics. With a very high mortality rate, (i.e., about 60% of the reported cases), H5N1AV influenza, commonly known as bird flu, is thought to be an important potential threat for a new pandemic. Because of this, several vaccines have been developed, but only a few million doses are readily available. Other zoonotic influenza strains, particularly in pigs, also threaten, and vaccines are being produced for them as well. In the event of an influenza pandemic, utilizing these resources optimally could make the difference between dealing with a serious infectious disease at a global scale and reducing it to a highly localized and controlled outbreak. In this paper, we address this issue by developing a mathematical model of influenza transmission on a network of cities. We couple the model with an optimization algorithm to allocate vaccine in time and space through the network. We find that our optimal allocation strategies can mitigate a pandemic, provided that vaccination occurs quickly, within the first weeks of a potential pandemic. In addition, our analysis highlights the importance of cooperative and coordinated vaccine distribution, if we want to mitigate a pandemic.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1002964
    DOI: 10.1371/journal.pcbi.1002964
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    References listed on IDEAS

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    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. T Déirdre Hollingsworth & Don Klinkenberg & Hans Heesterbeek & Roy M Anderson, 2011. "Mitigation Strategies for Pandemic Influenza A: Balancing Conflicting Policy Objectives," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-11, February.
    3. Ozgur Araz & Alison Galvani & Lauren Meyers, 2012. "Geographic prioritization of distributing pandemic influenza vaccines," Health Care Management Science, Springer, vol. 15(3), pages 175-187, September.
    4. Steven Riley & Joseph T Wu & Gabriel M Leung, 2007. "Optimizing the Dose of Pre-Pandemic Influenza Vaccines to Reduce the Infection Attack Rate," PLOS Medicine, Public Library of Science, vol. 4(6), pages 1-9, June.
    5. Masaki Imai & Tokiko Watanabe & Masato Hatta & Subash C. Das & Makoto Ozawa & Kyoko Shinya & Gongxun Zhong & Anthony Hanson & Hiroaki Katsura & Shinji Watanabe & Chengjun Li & Eiryo Kawakami & Shinya , 2012. "Experimental adaptation of an influenza H5 HA confers respiratory droplet transmission to a reassortant H5 HA/H1N1 virus in ferrets," Nature, Nature, vol. 486(7403), pages 420-428, June.
    6. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Policy responses > Vaccination
    2. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Allocation and rationing

    Citations

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    Cited by:

    1. Westerink-Duijzer, L.E. & van Jaarsveld, W.L. & Wallinga, J. & Dekker, R., 2015. "Dose-optimal vaccine allocation over multiple populations," Econometric Institute Research Papers EI2015-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "Literature review: The vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 268(1), pages 174-192.
    3. Qin, Wenjie & Tang, Sanyi & Xiang, Changcheng & Yang, Yali, 2016. "Effects of limited medical resource on a Filippov infectious disease model induced by selection pressure," Applied Mathematics and Computation, Elsevier, vol. 283(C), pages 339-354.
    4. Zéphirin Nganmeni & Roland Pongou & Bertrand Tchantcho & Jean‐Baptiste Tondji, 2022. "Vaccine and inclusion," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 24(5), pages 1101-1123, October.
      • Zéphirin Nganmeni & Roland Pongou & Bertrand Tchantcho & Jean‐baptiste Tondji, 2022. "Vaccine and inclusion," Post-Print hal-04257703, HAL.
      • Zéphirin Nganmeni & Roland Pongou & Bertrand Tchantcho & Jean-Baptiste Tondji, 2022. "Vaccine and Inclusion," Working Papers 2202E Classification-C62,, University of Ottawa, Department of Economics.
    5. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "The benefits of combining early aspecific vaccination with later specific vaccination," European Journal of Operational Research, Elsevier, vol. 271(2), pages 606-619.
    6. Anahideh, Hadis & Kang, Lulu & Nezami, Nazanin, 2022. "Fair and diverse allocation of scarce resources," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    7. Fadaki, Masih & Abareshi, Ahmad & Far, Shaghayegh Maleki & Lee, Paul Tae-Woo, 2022. "Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    8. Qin, Wenjie & Tang, Sanyi, 2014. "The selection pressures induced non-smooth infectious disease model and bifurcation analysis," Chaos, Solitons & Fractals, Elsevier, vol. 69(C), pages 160-171.

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