IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0287389.html
   My bibliography  Save this article

Multivariate epidemic count time series model

Author

Listed:
  • Shinsuke Koyama

Abstract

An infectious disease spreads not only over a single population or community but also across multiple and heterogeneous communities. Moreover, its transmissibility varies over time because of various factors such as seasonality and epidemic control, which results in strongly nonstationary behavior. In conventional methods for assessing transmissibility trends or changes, univariate time-varying reproduction numbers are calculated without taking into account transmission across multiple communities. In this paper, we propose a multivariate-count time series model for epidemics. We also propose a statistical method for estimating the transmission of infections across multiple communities and the time-varying reproduction numbers of each community simultaneously from a multivariate time series of case counts. We apply our method to incidence data for the novel coronavirus disease 2019 (COVID-19) pandemic to reveal the spatiotemporal heterogeneity of the epidemic process.

Suggested Citation

  • Shinsuke Koyama, 2023. "Multivariate epidemic count time series model," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-12, June.
    • Handle: RePEc:plo:pone00:0287389
    DOI: 10.1371/journal.pone.0287389
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0287389
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0287389&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0287389?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. repec:plo:pone00:0000758 is not listed on IDEAS
    2. Luís M A Bettencourt & Ruy M Ribeiro, 2008. "Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases," PLOS ONE, Public Library of Science, vol. 3(5), pages 1-9, May.
    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. 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.
    2. Kris V. Parag & Robin N. Thompson & Christl A. Donnelly, 2022. "Are epidemic growth rates more informative than reproduction numbers?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 5-15, November.
    3. Chad Cotti & Bryan Engelhardt & Joshua Foster & Erik Nesson & Paul Niekamp, 2021. "The relationship between in‐person voting and COVID‐19: Evidence from the Wisconsin primary," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 760-777, October.
    4. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    5. Victor W. Chu & Raymond K. Wong & Chi-Hung Chi & Wei Zhou & Ivan Ho, 2017. "The design of a cloud-based tracker platform based on system-of-systems service architecture," Information Systems Frontiers, Springer, vol. 19(6), pages 1283-1299, December.
    6. Kernel Prieto & M Victoria Chávez–Hernández & Jhoana P Romero–Leiton, 2022. "On mobility trends analysis of COVID–19 dissemination in Mexico City," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
    7. Schimit, P.H.T. & Monteiro, L.H.A., 2012. "On estimating the basic reproduction number in distinct stages of a contagious disease spreading," Ecological Modelling, Elsevier, vol. 240(C), pages 156-160.
    8. repec:plo:pcbi00:1006546 is not listed on IDEAS
    9. Ida Johnsson & M. Hashem Pesaran & Cynthia Fan Yang, 2023. "Structural Econometric Estimation of the Basic Reproduction Number for Covid-19 across U.S. States and Selected Countries," CESifo Working Paper Series 10659, CESifo.
    10. Xin-Yu Zhang & Lan-Lan Yu & Wei-Yi Wang & Gui-Quan Sun & Jian-Cheng Lv & Tao Zhou & Quan-Hui Liu, 2024. "Estimating the time-varying effective reproduction number via Cycle Threshold-based Transformer," PLOS Computational Biology, Public Library of Science, vol. 20(12), pages 1-25, December.
    11. Kernel Prieto, 2022. "Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.
    12. Sean Elliott & Christian Gourieroux, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Papers 2012.11542, arXiv.org.
    13. Mostafa Adimy & Julien Molina & Laurent Pujo-Menjouet & Grégoire Ranson & Jianhong Wu, 2022. "Forecasting the Effect of Pre-Exposure Prophylaxis (PrEP) on HIV Propagation with a System of Differential–Difference Equations with Delay," Mathematics, MDPI, vol. 10(21), pages 1-24, November.
    14. Maeno, Yoshiharu, 2016. "Detecting a trend change in cross-border epidemic transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 73-81.
    15. Sánchez-Monroy, J.A. & Riascos-Ochoa, Javier & Bustos, Abel, 2024. "Parameter estimation in the stochastic SIR model via scaled geometric Brownian motion," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    16. Sean ELLIOTT & Christian GOURIEROUX, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Working Papers 2020-31, Center for Research in Economics and Statistics.
    17. José Ulises Márquez Urbina & Graciela González Farías & L Leticia Ramírez Ramírez & D Iván Rodríguez González, 2022. "A multi-source global-local model for epidemic management," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-26, January.
    18. Anup Malani & Satej Soman & Sabareesh Ramachandran & Alice Chen & Darius N. Lakdawalla, 2022. "Vaccine Allocation Priorities Using Disease Surveillance and Economic Data," NBER Working Papers 29682, National Bureau of Economic Research, Inc.
    19. Kyle S Hickmann & Geoffrey Fairchild & Reid Priedhorsky & Nicholas Generous & James M Hyman & Alina Deshpande & Sara Y Del Valle, 2015. "Forecasting the 2013–2014 Influenza Season Using Wikipedia," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-29, May.
    20. Park, Dojoon & Kang, Yong Joo & Eom, Young Ho, 2024. "Asset pricing tests for pandemic risk," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1314-1334.
    21. De Simone, Andrea & Piangerelli, Marco, 2020. "A Bayesian approach for monitoring epidemics in presence of undetected cases," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    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:pone00:0287389. 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.