IDEAS home Printed from https://ideas.repec.org/a/plo/ppat00/1000281.html
   My bibliography  Save this article

Estimation of Transmission Parameters of H5N1 Avian Influenza Virus in Chickens

Author

Listed:
  • Annemarie Bouma
  • Ivo Claassen
  • Ketut Natih
  • Don Klinkenberg
  • Christl A Donnelly
  • Guus Koch
  • Michiel van Boven

Abstract

Despite considerable research efforts, little is yet known about key epidemiological parameters of H5N1 highly pathogenic influenza viruses in their avian hosts. Here we show how these parameters can be estimated using a limited number of birds in experimental transmission studies. Our quantitative estimates, based on Bayesian methods of inference, reveal that (i) the period of latency of H5N1 influenza virus in unvaccinated chickens is short (mean: 0.24 days; 95% credible interval: 0.099–0.48 days); (ii) the infectious period of H5N1 virus in unvaccinated chickens is approximately 2 days (mean: 2.1 days; 95%CI: 1.8–2.3 days); (iii) the reproduction number of H5N1 virus in unvaccinated chickens need not be high (mean: 1.6; 95%CI: 0.90–2.5), although the virus is expected to spread rapidly because it has a short generation interval in unvaccinated chickens (mean: 1.3 days; 95%CI: 1.0–1.5 days); and (iv) vaccination with genetically and antigenically distant H5N2 vaccines can effectively halt transmission. Simulations based on the estimated parameters indicate that herd immunity may be obtained if at least 80% of chickens in a flock are vaccinated. We discuss the implications for the control of H5N1 avian influenza virus in areas where it is endemic.Author Summary: Outbreaks of highly pathogenic H5N1 avian influenza in poultry first occurred in China in 1996. Since that time, the virus has become endemic in Asia, and has been the cause of outbreaks in Africa and Europe. Although many aspects of H5N1 virus biology have been studied in detail, surprisingly little is known about the key epidemiological parameters of the virus in its avian hosts (the length of time from infection until a bird becomes infectious, the duration of infectiousness, how many birds each infectious bird will infect). In this paper we show, using experimental transmission studies with unvaccinated and vaccinated chickens, that H5N1 avian influenza induces a short duration of infectiousness (∼2 days) and a very short period of time from infection until infectiousness (∼0.25 day) in unvaccinated chickens. Furthermore, while transmission was efficient among unvaccinated birds, no bird-to-bird transmission was observed in vaccinated chickens. Our results indicate that it may be difficult to curb outbreaks by vaccination after an introduction in a flock has been detected. On the other hand, preventive vaccination could be effective in preventing virus introductions and limiting the size of outbreaks.

Suggested Citation

  • Annemarie Bouma & Ivo Claassen & Ketut Natih & Don Klinkenberg & Christl A Donnelly & Guus Koch & Michiel van Boven, 2009. "Estimation of Transmission Parameters of H5N1 Avian Influenza Virus in Chickens," PLOS Pathogens, Public Library of Science, vol. 5(1), pages 1-13, January.
    • Handle: RePEc:plo:ppat00:1000281
    DOI: 10.1371/journal.ppat.1000281
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1000281
    Download Restriction: no
    File URL: https://journals.plos.org/plospathogens/article/file?id=10.1371/journal.ppat.1000281&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.ppat.1000281?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. Helen J Wearing & Pejman Rohani & Matt J Keeling, 2005. "Appropriate Models for the Management of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 2(7), pages 1-1, July.
    2. P. D. O’Neill & G. O. Roberts, 1999. "Bayesian inference for partially observed stochastic epidemics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 121-129.
    3. J. C. de Jong & E. C. J. Claas & A. D. M. E. Osterhaus & R. G. Webster & W. L. Lim, 1997. "A pandemic warning?," Nature, Nature, vol. 389(6651), pages 554-554, October.
    4. M. F. Ducatez & C. M. Olinger & A. A. Owoade & S. De Landtsheer & W. Ammerlaan & H. G. M. Niesters & A. D. M. E. Osterhaus & R. A. M. Fouchier & C. P. Muller, 2006. "Multiple introductions of H5N1 in Nigeria," Nature, Nature, vol. 442(7098), pages 37-37, July.
    5. Michael Höhle & Erik Jørgensen & Philip D. O'Neill, 2005. "Inference in disease transmission experiments by using stochastic epidemic models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(2), pages 349-366, April.
    6. Nicholas J. Savill & Suzanne G. St Rose & Matthew J. Keeling & Mark E. J. Woolhouse, 2006. "Silent spread of H5N1 in vaccinated poultry," Nature, Nature, vol. 442(7104), pages 757-757, August.
    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. Kim M Pepin & Jia Wang & Colleen T Webb & Jennifer A Hoeting & Mary Poss & Peter J Hudson & Wenshan Hong & Huachen Zhu & Yi Guan & Steven Riley, 2013. "Anticipating the Prevalence of Avian Influenza Subtypes H9 and H5 in Live-Bird Markets," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-8, February.
    2. Alyssa Marchese & Alice Hovorka, 2022. "Zoonoses Transfer, Factory Farms and Unsustainable Human–Animal Relations," Sustainability, MDPI, vol. 14(19), pages 1-10, October.
    3. Moyen, Natalie & Hoque, Md Ahasanul & Mahmud, Rashed & Hasan, Mahmudul & Sarkar, Sudipta & Biswas, Paritosh Kumar & Mehedi, Hossain & Henning, Joerg & Mangtani, Punam & Flora, Meerjady Sabrina & Rahma, 2021. "Avian influenza transmission risk along live poultry trading networks in Bangladesh," LSE Research Online Documents on Economics 112514, London School of Economics and Political Science, LSE Library.
    4. Francesco Pinotti & Lisa Kohnle & José Lourenço & Sunetra Gupta & Md. Ahasanul Hoque & Rashed Mahmud & Paritosh Biswas & Dirk Pfeiffer & Guillaume Fournié, 2024. "Modelling the transmission dynamics of H9N2 avian influenza viruses in a live bird market," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

    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. Ross, J.V. & Pagendam, D.E. & Pollett, P.K., 2009. "On parameter estimation in population models II: Multi-dimensional processes and transient dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 123-132.
    2. Lahrouz, A. & El Mahjour, H. & Settati, A. & Bernoussi, A., 2018. "Dynamics and optimal control of a non-linear epidemic model with relapse and cure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 299-317.
    3. Kypraios, Theodore, 2009. "A note on maximum likelihood estimation of the initial number of susceptibles in the general stochastic epidemic model," Statistics & Probability Letters, Elsevier, vol. 79(18), pages 1972-1976, September.
    4. McKinley, Trevelyan J. & Ross, Joshua V. & Deardon, Rob & Cook, Alex R., 2014. "Simulation-based Bayesian inference for epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 434-447.
    5. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    6. Claire Hautefeuille & Gwenaëlle Dauphin & Marisa Peyre, 2020. "Knowledge and remaining gaps on the role of animal and human movements in the poultry production and trade networks in the global spread of avian influenza viruses – A scoping review," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    7. Richard C. Larson, 2007. "Simple Models of Influenza Progression Within a Heterogeneous Population," Operations Research, INFORMS, vol. 55(3), pages 399-412, June.
    8. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
    9. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2016. "Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-18, April.
    10. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    11. Céline Christiansen-Jucht & Kamil Erguler & Chee Yan Shek & María-Gloria Basáñez & Paul E. Parham, 2015. "Modelling Anopheles gambiae s.s. Population Dynamics with Temperature- and Age-Dependent Survival," IJERPH, MDPI, vol. 12(6), pages 1-31, May.
    12. Rozan, E.A. & Kuperman, M.N. & Bouzat, S., 2024. "The importance of the incubation time distribution in compartmental epidemiological models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 650(C).
    13. Victoria Chebotaeva & Paula A. Vasquez, 2023. "Erlang-Distributed SEIR Epidemic Models with Cross-Diffusion," Mathematics, MDPI, vol. 11(9), pages 1-18, May.
    14. Daniele Proverbio & Françoise Kemp & Stefano Magni & Andreas Husch & Atte Aalto & Laurent Mombaerts & Alexander Skupin & Jorge Gonçalves & Jose Ameijeiras-Alonso & Christophe Ley, 2021. "Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    15. Nik J Cunniffe & Francisco F Laranjeira & Franco M Neri & R Erik DeSimone & Christopher A Gilligan, 2014. "Cost-Effective Control of Plant Disease When Epidemiological Knowledge Is Incomplete: Modelling Bahia Bark Scaling of Citrus," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-14, August.
    16. Miclo, Laurent & Weibull, Jörgen W. & Spiro, Daniel, 2020. "Optimal epidemic suppression under an ICU constraint," TSE Working Papers 20-1111, Toulouse School of Economics (TSE).
    17. Elisabeta Vergu & Henri Busson & Pauline Ezanno, 2010. "Impact of the Infection Period Distribution on the Epidemic Spread in a Metapopulation Model," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-16, February.
    18. Bekiros, Stelios & Kouloumpou, Dimitra, 2020. "SBDiEM: A new mathematical model of infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    19. Carbone, Giuseppe & De Vincenzo, Ilario, 2022. "A general theory for infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    20. Golightly, Andrew & Bradley, Emma & Lowe, Tom & Gillespie, Colin S., 2019. "Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 92-107.

    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:ppat00:1000281. 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: plospathogens (email available below). General contact details of provider: https://journals.plos.org/plospathogens .

    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.