IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1006875.html
   My bibliography  Save this article

A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus

Author

Listed:
  • Sifat A Moon
  • Lee W Cohnstaedt
  • D Scott McVey
  • Caterina M Scoglio

Abstract

West Nile virus (WNV)—a mosquito-borne arbovirus—entered the USA through New York City in 1999 and spread to the contiguous USA within three years while transitioning from epidemic outbreaks to endemic transmission. The virus is transmitted by vector competent mosquitoes and maintained in the avian populations. WNV spatial distribution is mainly determined by the movement of residential and migratory avian populations. We developed an individual-level heterogeneous network framework across the USA with the goal of understanding the long-range spatial distribution of WNV. To this end, we proposed three distance dispersal kernels model: 1) exponential—short-range dispersal, 2) power-law—long-range dispersal in all directions, and 3) power-law biased by flyway direction —long-range dispersal only along established migratory routes. To select the appropriate dispersal kernel we used the human case data and adopted a model selection framework based on approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). From estimated parameters, we find that the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypothesis of long-range WNV transmission is mainly along the migratory bird flyways. Through extensive simulation from 2014 to 2016, we proposed and tested hypothetical mitigation strategies and found that mosquito population reduction in the infected states and neighboring states is potentially cost-effective.Author summary: The underlying pattern of West Nile virus (WNV) geographic spread across the United States is not completely clear, which is a necessary step for continental or state level mitigation strategies to reduce WNV transmission. We report a network model that explains the geographic spread of WNV in the United States. West Nile virus is a mosquito-borne pathogen that infects many avian species with different movement ranges. From our research, we found that migration patterns and routes play an essential role in the WNV spatial distribution. The virus spreads in all directions at short distances because of local birds and short-distance migratory birds. However, the virus also disperses long distances along the avian migratory routes. Our model is designed to be flexible and therefore can be used to explore spreading patterns of other infectious diseases in other geographic locations.

Suggested Citation

  • Sifat A Moon & Lee W Cohnstaedt & D Scott McVey & Caterina M Scoglio, 2019. "A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    • Handle: RePEc:plo:pcbi00:1006875
    DOI: 10.1371/journal.pcbi.1006875
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006875
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006875&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1006875?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. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. Mikael Sunnåker & Alberto Giovanni Busetto & Elina Numminen & Jukka Corander & Matthieu Foll & Christophe Dessimoz, 2013. "Approximate Bayesian Computation," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-10, January.
    3. Ellen Brooks-Pollock & Gareth O. Roberts & Matt J. Keeling, 2014. "A dynamic model of bovine tuberculosis spread and control in Great Britain," Nature, Nature, vol. 511(7508), pages 228-231, July.
    4. Nicholas B. DeFelice & Eliza Little & Scott R. Campbell & Jeffrey Shaman, 2017. "Ensemble forecast of human West Nile virus cases and mosquito infection rates," Nature Communications, Nature, vol. 8(1), pages 1-6, April.
    5. Jean-Michel Marin & Natesh S. Pillai & Christian P. Robert & Judith Rousseau, 2014. "Relevant statistics for Bayesian model choice," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(5), pages 833-859, November.
    6. repec:dau:papers:123456789/7848 is not listed on IDEAS
    7. Samuel Soubeyrand & Leonhard Held & Michael Höhle & Ivan Sache, 2008. "Modelling the spread in space and time of an airborne plant disease," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 253-272, June.
    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. Bhowmick, Suman & Gethmann, Jörn & Conraths, Franz J. & Sokolov, Igor M. & Lentz, Hartmut H.K., 2023. "SEIR-Metapopulation model of potential spread of West Nile virus," Ecological Modelling, Elsevier, vol. 476(C).

    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. Warne, David J. & Baker, Ruth E. & Simpson, Matthew J., 2018. "Multilevel rejection sampling for approximate Bayesian computation," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 71-86.
    2. D.T. Frazier & G.M. Martin & C.P. Robert & J. Rousseau, 2016. "Asymptotic Properties of Approximate Bayesian Computation," Monash Econometrics and Business Statistics Working Papers 18/16, Monash University, Department of Econometrics and Business Statistics.
    3. S. G. J. Senarathne & C. C. Drovandi & J. M. McGree, 2020. "Bayesian sequential design for Copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 454-478, June.
    4. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, vol. 4(1), pages 1-33, March.
    5. James Martin & Ajay Jasra & Emma McCoy, 2013. "Inference for a class of partially observed point process models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 413-437, June.
    6. Mark Bognanni & John Zito, 2019. "Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility," Working Papers 19-29, Federal Reserve Bank of Cleveland.
    7. Landry, C. & Bonnot, F. & Ravigné, V. & Carlier, J. & Rengifo, D. & Vaillant, J. & Abadie, C., 2017. "A foliar disease simulation model to assist the design of new control methods against black leaf streak disease of banana," Ecological Modelling, Elsevier, vol. 359(C), pages 383-397.
    8. George Karabatsos, 2023. "Approximate Bayesian computation using asymptotically normal point estimates," Computational Statistics, Springer, vol. 38(2), pages 531-568, June.
    9. Saifuddin Syed & Alexandre Bouchard‐Côté & George Deligiannidis & Arnaud Doucet, 2022. "Non‐reversible parallel tempering: A scalable highly parallel MCMC scheme," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 321-350, April.
    10. Brignone, Riccardo & Gonzato, Luca & Lütkebohmert, Eva, 2023. "Efficient Quasi-Bayesian Estimation of Affine Option Pricing Models Using Risk-Neutral Cumulants," Journal of Banking & Finance, Elsevier, vol. 148(C).
    11. Lee Anthony & Caron Francois & Doucet Arnaud & Holmes Chris, 2012. "Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-31, January.
    12. David J Price & Alexandre Breuzé & Richard Dybowski & Piero Mastroeni & Olivier Restif, 2017. "An efficient moments-based inference method for within-host bacterial infection dynamics," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-27, November.
    13. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    14. Beirne, John & Villafuerte, James & Zhang, Bryan (ed.), 2022. "Fintech and COVID-19: Impacts, Challenges, and Policy Priorities for Asia," ADBI Books, Asian Development Bank Institute, number 29, Décembre.
    15. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
    16. Naoki Awaya & Yasuhiro Omori, 2021. "Particle Rolling MCMC with Double-Block Sampling ," CIRJE F-Series CIRJE-F-1175, CIRJE, Faculty of Economics, University of Tokyo.
    17. Daniel Silk & Paul D W Kirk & Chris P Barnes & Tina Toni & Michael P H Stumpf, 2014. "Model Selection in Systems Biology Depends on Experimental Design," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-14, June.
    18. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2018. "Monte Carlo Confidence Sets for Identified Sets," Econometrica, Econometric Society, vol. 86(6), pages 1965-2018, November.
    19. Henri Pesonen & Umberto Simola & Alvaro Köhn‐Luque & Henri Vuollekoski & Xiaoran Lai & Arnoldo Frigessi & Samuel Kaski & David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Jukka Corander, 2023. "ABC of the future," International Statistical Review, International Statistical Institute, vol. 91(2), pages 243-268, August.
    20. Frederick Callaway & Antonio Rangel & Thomas L Griffiths, 2021. "Fixation patterns in simple choice reflect optimal information sampling," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-29, 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:pcbi00:1006875. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.