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Mitigation Strategies for Pandemic Influenza A: Balancing Conflicting Policy Objectives

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  • T Déirdre Hollingsworth
  • Don Klinkenberg
  • Hans Heesterbeek
  • Roy M Anderson

Abstract

Mitigation of a severe influenza pandemic can be achieved using a range of interventions to reduce transmission. Interventions can reduce the impact of an outbreak and buy time until vaccines are developed, but they may have high social and economic costs. The non-linear effect on the epidemic dynamics means that suitable strategies crucially depend on the precise aim of the intervention. National pandemic influenza plans rarely contain clear statements of policy objectives or prioritization of potentially conflicting aims, such as minimizing mortality (depending on the severity of a pandemic) or peak prevalence or limiting the socio-economic burden of contact-reducing interventions. We use epidemiological models of influenza A to investigate how contact-reducing interventions and availability of antiviral drugs or pre-pandemic vaccines contribute to achieving particular policy objectives. Our analyses show that the ideal strategy depends on the aim of an intervention and that the achievement of one policy objective may preclude success with others, e.g., constraining peak demand for public health resources may lengthen the duration of the epidemic and hence its economic and social impact. Constraining total case numbers can be achieved by a range of strategies, whereas strategies which additionally constrain peak demand for services require a more sophisticated intervention. If, for example, there are multiple objectives which must be achieved prior to the availability of a pandemic vaccine (i.e., a time-limited intervention), our analysis shows that interventions should be implemented several weeks into the epidemic, not at the very start. This observation is shown to be robust across a range of constraints and for uncertainty in estimates of both R0 and the timing of vaccine availability. These analyses highlight the need for more precise statements of policy objectives and their assumed consequences when planning and implementing strategies to mitigate the impact of an influenza pandemic.Author Summary: In the event of an influenza pandemic which has high mortality and the potential to spread rapidly, such as the 1918–19 pandemic, there are a number of non-pharmaceutical public health control options available to reduce transmission in the community and mitigate the effects of the pandemic. These include reducing social contacts by closing schools or postponing public events, and encouraging hand washing and the use of masks. These interventions will not only have a non-intuitive impact on the epidemic dynamics, but they will also have direct and indirect social and economic costs, which mean that governments will only want to use them for a limited amount of time. We use simulations to show that limited-time interventions that achieve one aim, e.g., contain the total number of cases below some maximum number of treatments available, are not the same as those that achieve another, e.g., minimize peak demand for health care services. If multiple aims are defined simultaneously, we often see that the optimal intervention need not commence immediately but can begin a few weeks into the epidemic. Our research demonstrates the importance of tailoring pandemic plans to defined policy targets with some flexibility to allow for uncertainty in the characteristics of the pandemic.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1001076
    DOI: 10.1371/journal.pcbi.1001076
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    1. Basco, Sergi & Domènech, Jordi & Rosés, Joan R., 2021. "The redistributive effects of pandemics: Evidence on the Spanish flu," World Development, Elsevier, vol. 141(C).
    2. Basco, Sergi & Domènech, Jordi & Rosés, Joan R., 2021. "The redistributive effects of pandemics: Evidence on the Spanish flu," World Development, Elsevier, vol. 141(C).
    3. Amit Summan & Arindam Nandi, 2022. "Timing of non-pharmaceutical interventions to mitigate COVID-19 transmission and their effects on mobility: a cross-country analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(1), pages 105-117, February.
    4. Andrew G. Atkeson & Karen A. Kopecky & Tao Zha, 2024. "Four Stylized Facts About Covid‐19," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(1), pages 3-42, February.
    5. Hasnan Baber, 2021. "Efficacy of COVID-19 screening system and customer satisfaction in banks: moderating role of the perceived threat and health risk," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 26(4), pages 295-304, December.
    6. David E. Bloom & Michael Kuhn & Klaus Prettner, 2022. "Modern Infectious Diseases: Macroeconomic Impacts and Policy Responses," Journal of Economic Literature, American Economic Association, vol. 60(1), pages 85-131, March.
    7. Esra Ozdenerol & Rebecca Michelle Bingham-Byrne & Jacob Seboly, 2023. "Female Leadership during COVID-19: The Effectiveness of Diverse Approaches towards Mitigation Management during a Pandemic," IJERPH, MDPI, vol. 20(21), pages 1-36, November.
    8. Po Yang & Jun Qi & Shuhao Zhang & Xulong Wang & Gaoshan Bi & Yun Yang & Bin Sheng & Geng Yang, 2020. "Feasibility study of mitigation and suppression strategies for controlling COVID-19 outbreaks in London and Wuhan," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-19, August.
    9. Hensel, Lukas & Witte, Marc & Caria, A. Stefano & Fetzer, Thiemo & Fiorin, Stefano & Götz, Friedrich M. & Gomez, Margarita & Haushofer, Johannes & Ivchenko, Andriy & Kraft-Todd, Gordon & Reutskaja, El, 2022. "Global Behaviors, Perceptions, and the Emergence of Social Norms at the Onset of the COVID-19 Pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 193(C), pages 473-496.
    10. 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.
    11. Silva, Maria Laura & Perrier, Lionel & Cohen, Jean Marie & Paget, William John & Mosnier, Anne & Späth, Hans Martin, 2015. "A literature review to identify factors that determine policies for influenza vaccination," Health Policy, Elsevier, vol. 119(6), pages 697-708.
    12. Andy Dobson & Cristiano Ricci & Raouf Boucekkine & Giorgio Fabbri & Ted Loch-Temzelides & Mercedes Pascual, 2023. "Balancing economic and epidemiological interventions in the early stages of pathogen emergence," Post-Print hal-04150117, HAL.
    13. Claudio Neidhöfer & Guido Neidhöfer, 2020. "The Effectiveness of School Closures and Other Pre-Lockdown COVID-19 Mitigation Strategies in Argentina, Italy, and South Korea," CEDLAS, Working Papers 0266, CEDLAS, Universidad Nacional de La Plata.
    14. Francesco Di Lauro & István Z Kiss & Joel C Miller, 2021. "Optimal timing of one-shot interventions for epidemic control," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-24, March.
    15. Çaparoğlu, Ömer Faruk & Ok, Yeşim & Tutam, Mahmut, 2021. "To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    16. Pierre-Alexandre Bliman & Michel Duprez & Yannick Privat & Nicolas Vauchelet, 2021. "Optimal Immunity Control and Final Size Minimization by Social Distancing for the SIR Epidemic Model," Journal of Optimization Theory and Applications, Springer, vol. 189(2), pages 408-436, May.
    17. Brandon Lieberthal & Allison M Gardner, 2021. "Connectivity, reproduction number, and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-22, March.
    18. Ayaz Hyder & David L Buckeridge & Brian Leung, 2013. "Predictive Validation of an Influenza Spread Model," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-20, June.

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