IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0056157.html
   My bibliography  Save this article

Anticipating the Prevalence of Avian Influenza Subtypes H9 and H5 in Live-Bird Markets

Author

Listed:
  • Kim M Pepin
  • Jia Wang
  • Colleen T Webb
  • Jennifer A Hoeting
  • Mary Poss
  • Peter J Hudson
  • Wenshan Hong
  • Huachen Zhu
  • Yi Guan
  • Steven Riley

Abstract

An ability to forecast the prevalence of specific subtypes of avian influenza viruses (AIV) in live-bird markets would facilitate greatly the implementation of preventative measures designed to minimize poultry losses and human exposure. The minimum requirement for developing predictive quantitative tools is surveillance data of AIV prevalence sampled frequently over several years. Recently, a 4-year time series of monthly sampling of hemagglutinin subtypes 1–13 in ducks, chickens and quail in live-bird markets in southern China has become available. We used these data to investigate whether a simple statistical model, based solely on historical data (variables such as the number of positive samples in host X of subtype Y time t months ago), could accurately predict prevalence of H5 and H9 subtypes in chickens. We also examined the role of ducks and quail in predicting prevalence in chickens within the market setting because between-species transmission is thought to occur within markets but has not been measured. Our best statistical models performed remarkably well at predicting future prevalence (pseudo-R2 = 0.57 for H9 and 0.49 for H5), especially considering the multi-host, multi-subtype nature of AIVs. We did not find prevalence of H5/H9 in ducks or quail to be predictors of prevalence in chickens within the Chinese markets. Our results suggest surveillance protocols that could enable more accurate and timely predictive statistical models. We also discuss which data should be collected to allow the development of mechanistic models.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0056157
    DOI: 10.1371/journal.pone.0056157
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0056157
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0056157&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0056157?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. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    2. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    3. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    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. Christian Balcells, 2022. "Determinants of firm boundaries and organizational performance: an empirical investigation of the Chilean truck market," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 423-461, April.
    2. Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    3. Addisu Jember Zeleke & Serena Moscato & Rossella Miglio & Lorenzo Chiari, 2022. "Length of Stay Analysis of COVID-19 Hospitalizations Using a Count Regression Model and Quantile Regression: A Study in Bologna, Italy," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    4. Taghouti, Ibtissem & Martinez-Gomez, Victor & Coque, José María Garcia Alvarez, 2015. "Exploring Eu Food Safety Notifications On Agro-Food Imports: Are Mediterranean Partner Countries Discriminated?," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 3(2), pages 1-15, April.
    5. Počuča, Nikola & Jevtić, Petar & McNicholas, Paul D. & Miljkovic, Tatjana, 2020. "Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 79-93.
    6. Taghouti, Ibtissem & Martinez-Gomez, Victor & Marti, Luisa, 2017. "Sanitary and Phytosanitary measures in agri-food imports from the European Union: Reputation effects over time," Economia Agraria y Recursos Naturales, Spanish Association of Agricultural Economists, vol. 16(02), January.
    7. Truong, Chi & Sheen, Jeffrey & Trück, Stefan & Villafuerte, James, 2022. "Early warning systems using dynamic factor models: An application to Asian economies," Journal of Financial Stability, Elsevier, vol. 58(C).
    8. Ayan CHOWDHURY & Soma Chowdhury BISWAS, 2018. "Appropriate statistical model for zero-inflated count data: simulation based study," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 6(1), pages 5-12, June.
    9. Kristian Kleinke & Jost Reinecke, 2013. "Multiple imputation of incomplete zero-inflated count data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 311-336, August.
    10. Shi, Peng & Feng, Xiaoping & Ivantsova, Anastasia, 2015. "Dependent frequency–severity modeling of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 417-428.
    11. Lorena Tudela-Marco & Jose Maria Garcia-Alvarez-Coque & Luisa Martí-Selva, 2017. "Do EU Member States Apply Food Standards Uniformly? A Look at Fruit and Vegetable Safety Notifications," Journal of Common Market Studies, Wiley Blackwell, vol. 55(2), pages 387-405, March.
    12. Kessler, Jeff & Sperling, Daniel, 2016. "Tracking U.S. biofuel innovation through patents," Energy Policy, Elsevier, vol. 98(C), pages 97-107.
    13. Henrik Basche, 2022. "Determinants of cross-border co-patents: empirical evidence from 45 European regions [Determinanten grenzüberschreitender Ko-Patentaktivitäten: Empirische Befunde aus 45 europäischen Regionen]," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(1), pages 1-22, April.
    14. Fabrice Gilles & Sabina Issehnane & Florent Sari, 2022. "Using short-term jobs as a way to find a regular job. What kind of role for local context?," TEPP Working Paper 2022-07, TEPP.
    15. repec:hal:spmain:info:hdl:2441/dambferfb7dfprc9m052g20qh is not listed on IDEAS
    16. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    17. Cornelia Lawson, 2013. "Academic Inventions Outside the University: Investigating Patent Ownership in the UK," Industry and Innovation, Taylor & Francis Journals, vol. 20(5), pages 385-398, July.
    18. Vipin Arora & Shuping Shi, 2016. "Nonlinearities and tests of asset price bubbles," Empirical Economics, Springer, vol. 50(4), pages 1421-1433, June.
    19. Luiz Paulo Fávero & Joseph F. Hair & Rafael de Freitas Souza & Matheus Albergaria & Talles V. Brugni, 2021. "Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships," Mathematics, MDPI, vol. 9(10), pages 1-28, May.
    20. Da Fonseca José & Grasselli Martino & Ielpo Florian, 2014. "Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 1-37, May.
    21. Hansen, Lars Peter & Heaton, John & Luttmer, Erzo G J, 1995. "Econometric Evaluation of Asset Pricing Models," Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 237-274.

    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:pone00:0056157. 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.