IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0067253.html
   My bibliography  Save this article

Using a Dynamic Model to Consider Optimal Antiviral Stockpile Size in the Face of Pandemic Influenza Uncertainty

Author

Listed:
  • Amy L Greer
  • Dena Schanzer

Abstract

Background: The Canadian National Antiviral Stockpile (NAS) contains treatment for 17.5% of Canadians. This assumes no concurrent intervention strategies and no wastage due to non-influenza respiratory infections. A dynamic model can provide a mechanism to consider complex scenarios to support decisions regarding the optimal NAS size under uncertainty. Methods: We developed a dynamic model for pandemic influenza in Canada that is structured by age and risk to calculate the demand for antivirals to treat persons with pandemic influenza under a wide-range of scenarios that incorporated transmission dynamics, disease severity, and intervention strategies. The anticipated per capita number of acute respiratory infections due to viruses other than influenza was estimated for the full pandemic period from surveys based on criteria to identify potential respiratory infections. Results: Our results demonstrate that up to two thirds of the population could develop respiratory symptoms as a result of infection with a pandemic strain. In the case of perfect antiviral allocation, up to 39.8% of the population could request antiviral treatment. As transmission dynamics, severity and timing of the emergence of a novel influenza strain are unknown, the sensitivity analysis produced considerable variation in potential demand (median: 11%, IQR: 2–21%). If the next pandemic strain emerges in late spring or summer and a vaccine is available before the anticipated fall wave, the median prediction was reduced to 6% and IQR to 0.7–14%. Under the strategy of offering empirical treatment to all patients with influenza like symptoms who present for care, demand could increase to between 65 and 144%. Conclusions: The demand for antivirals during a pandemic is uncertain. Unless an accurate, timely and cost-effective test is available to identify influenza cases, demand for antivirals from persons infected with other respiratory viruses will be substantial and have a significant impact on the NAS.

Suggested Citation

  • Amy L Greer & Dena Schanzer, 2013. "Using a Dynamic Model to Consider Optimal Antiviral Stockpile Size in the Face of Pandemic Influenza Uncertainty," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.
    • Handle: RePEc:plo:pone00:0067253
    DOI: 10.1371/journal.pone.0067253
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0067253
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0067253&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0067253?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Niels G Becker & Dingcheng Wang, 2011. "Can Antiviral Drugs Contain Pandemic Influenza Transmission?," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-9, March.
    2. Xinzhi Zhang & Martin I. Meltzer & Pascale M. Wortley, 2006. "FluSurge—A Tool to Estimate Demand for Hospital Services during the Next Pandemic Influenza," Medical Decision Making, , vol. 26(6), pages 617-623, November.
    3. Christina E. Mills & James M. Robins & Marc Lipsitch, 2004. "Transmissibility of 1918 pandemic influenza," Nature, Nature, vol. 432(7019), pages 904-906, December.
    4. Nedialko B Dimitrov & Sebastian Goll & Nathaniel Hupert & Babak Pourbohloul & Lauren Ancel Meyers, 2011. "Optimizing Tactics for Use of the U.S. Antiviral Strategic National Stockpile for Pandemic Influenza," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
    5. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
    6. Carrasco, L R & Lee, V J & Chen, M I & Matchar, D B & Thompson, J P & Cook, A R, 2011. "Strategies for antiviral stockpiling for future influenza pandemics: a global epidemic-economic perspective," MPRA Paper 57763, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaowei Chen & Wing Fung Chong & Runhuan Feng & Linfeng Zhang, 2020. "Pandemic risk management: resources contingency planning and allocation," Papers 2012.03200, arXiv.org.
    2. Chen, Xiaowei & Chong, Wing Fung & Feng, Runhuan & Zhang, Linfeng, 2021. "Pandemic risk management: Resources contingency planning and allocation," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 359-383.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gillis, Melissa & Urban, Ryley & Saif, Ahmed & Kamal, Noreen & Murphy, Matthew, 2021. "A simulation–optimization framework for optimizing response strategies to epidemics," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. Brotherhood, Luiz & Kircher, Philipp & Santos, Cezar & Tertilt, Michèle, 2023. "Optimal Age-based Policies for Pandemics: An Economic Analysis of Covid-19 and Beyond," IDB Publications (Working Papers) 13295, Inter-American Development Bank.
    3. Brotherhood, Luiz & Kircher, Philipp & Santos, Cezar & Tertilt, Michele, 2024. "Optimal Age-based Policies for Pandemics: An Economic Analysis of Covid-19 and Beyond," LIDAM Discussion Papers CORE 2024012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    5. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
    6. M. Hashem Pesaran & Cynthia Fan Yang, 2022. "Matching theory and evidence on Covid‐19 using a stochastic network SIR model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1204-1229, September.
    7. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    8. Houštecká, Anna & Koh, Dongya & Santaeulàlia-Llopis, Raül, 2021. "Contagion at work: Occupations, industries and human contact," Journal of Public Economics, Elsevier, vol. 200(C).
    9. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    10. Aditya Goenka & Lin Liu, 2012. "Infectious diseases and endogenous fluctuations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 50(1), pages 125-149, May.
    11. Christoph Zimmer & Reza Yaesoubi & Ted Cohen, 2017. "A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-21, January.
    12. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    13. S. M. Niaz Arifin & Christoph Zimmer & Caroline Trotter & Anaïs Colombini & Fati Sidikou & F. Marc LaForce & Ted Cohen & Reza Yaesoubi, 2019. "Cost-Effectiveness of Alternative Uses of Polyvalent Meningococcal Vaccines in Niger: An Agent-Based Transmission Modeling Study," Medical Decision Making, , vol. 39(5), pages 553-567, July.
    14. Clarke, Lorcan, 2020. "An introduction to economic studies, health emergencies, and COVID-19," LSE Research Online Documents on Economics 105051, London School of Economics and Political Science, LSE Library.
    15. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    16. Mirjam Kretzschmar & Rafael T Mikolajczyk, 2009. "Contact Profiles in Eight European Countries and Implications for Modelling the Spread of Airborne Infectious Diseases," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-8, June.
    17. Andrei I. Vlad & Alexei A. Romanyukha & Tatiana E. Sannikova, 2024. "Parameter Tuning of Agent-Based Models: Metaheuristic Algorithms," Mathematics, MDPI, vol. 12(14), pages 1-21, July.
    18. Elisabetta De Cao & Alessia Melegaro & Rogier Klok & Maarten Postma, 2014. "Optimising Assessments of the Epidemiological Impact in the Netherlands of Paediatric Immunisation with 13-Valent Pneumococcal Conjugate Vaccine Using Dynamic Transmission Modelling," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    19. Richard Pitman & David Fisman & Gregory S. Zaric & Maarten Postma & Mirjam Kretzschmar & John Edmunds & Marc Brisson, 2012. "Dynamic Transmission Modeling," Medical Decision Making, , vol. 32(5), pages 712-721, September.
    20. Wiriya Mahikul & Somkid Kripattanapong & Piya Hanvoravongchai & Aronrag Meeyai & Sopon Iamsirithaworn & Prasert Auewarakul & Wirichada Pan-ngum, 2020. "Contact Mixing Patterns and Population Movement among Migrant Workers in an Urban Setting in Thailand," IJERPH, MDPI, vol. 17(7), pages 1-11, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0067253. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.