IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2008.06051.html

A Spatial Stochastic SIR Model for Transmission Networks with Application to COVID-19 Epidemic in China

Author

Listed:
  • Tatsushi Oka
  • Wei Wei
  • Dan Zhu

Abstract

Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time Markov model to analyze the propagation of COVID-19 across 33 provincial regions in China. This approach enables us to evaluate the effect of mobility restriction policies on the spread of the disease. We use data on daily human mobility across regions and apply the Bayesian framework to estimate the proposed model. The results show that the spread of the disease in China was predominately driven by community transmission within regions and the lockdown policy introduced by local governments curbed the spread of the pandemic. Further, we document that Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020, and the disease spread out to connected regions. The transmission from these epicenters substantially declined following the introduction of human mobility restrictions across regions.

Suggested Citation

  • Tatsushi Oka & Wei Wei & Dan Zhu, 2020. "A Spatial Stochastic SIR Model for Transmission Networks with Application to COVID-19 Epidemic in China," Papers 2008.06051, arXiv.org, revised Aug 2020.
    • Handle: RePEc:arx:papers:2008.06051
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2008.06051
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Philipson, Tomas, 2000. "Economic epidemiology and infectious diseases," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 33, pages 1761-1799, Elsevier.
    2. Mark Gersovitz & Jeffrey S. Hammer, 2004. "The Economical Control of Infectious Diseases," Economic Journal, Royal Economic Society, vol. 114(492), pages 1-27, January.
    3. Rowthorn, Robert & Toxvaerd, Flavio, 2012. "The Optimal Control of Infectious Diseases via Prevention and Treatment," CEPR Discussion Papers 8925, C.E.P.R. Discussion Papers.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Fernández-Villaverde, Jesús & Jones, Charles I., 2022. "Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    6. James E. Anderson, 2011. "The Gravity Model," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 133-160, September.
    7. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    8. Roman Jandarov & Murali Haran & Ottar Bjørnstad & Bryan Grenfell, 2014. "Emulating a gravity model to infer the spatiotemporal dynamics of an infectious disease," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(3), pages 423-444, April.
    9. Chib, Siddhartha & Greenberg, Edward & Winkelmann, Rainer, 1998. "Posterior simulation and Bayes factors in panel count data models," Journal of Econometrics, Elsevier, vol. 86(1), pages 33-54, June.
    10. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
    11. Wiemer, Calla, 1987. "Optimal disease control through combined use of preventive and curative measures," Journal of Development Economics, Elsevier, vol. 25(2), pages 301-319, April.
    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. Callaway, Brantly & Li, Tong, 2023. "Policy evaluation during a pandemic," Journal of Econometrics, Elsevier, vol. 236(1).

    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. Yasushi Iwamoto, 2021. "Welfare economics of managing an epidemic: an exposition," The Japanese Economic Review, Springer, vol. 72(4), pages 537-579, October.
    2. Joshua S. Gans, 2022. "The Economic Consequences of R= 1: Towards a Workable Behavioural Epidemiological Model of Pandemics," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 14(1), pages 3-25, April.
    3. David E. Bloom & Michael Kuhn & Klaus Prettner, 2022. "Modern Infectious Diseases: Macroeconomic Impacts and Policy Responses," Journal of Economic Literature, American Economic Association, vol. 60(1), pages 85-131, March.
    4. Rowthorn, Robert & Toxvaerd, Flavio, 2012. "The Optimal Control of Infectious Diseases via Prevention and Treatment," CEPR Discussion Papers 8925, C.E.P.R. Discussion Papers.
    5. La Torre, Davide & Malik, Tufail & Marsiglio, Simone, 2020. "Optimal control of prevention and treatment in a basic macroeconomic–epidemiological model," Mathematical Social Sciences, Elsevier, vol. 108(C), pages 100-108.
    6. Joshua S. Gans, 2023. "Vaccine Hesitancy, Passports, And The Demand For Vaccination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(2), pages 641-652, May.
    7. Gersovitz, Mark & Hammer, Jeffrey S., 2005. "Tax/subsidy policies toward vector-borne infectious diseases," Journal of Public Economics, Elsevier, vol. 89(4), pages 647-674, April.
    8. Matthew Goodkin-Gold & Michael Kremer & Christopher M. Snyder & Heidi Williams, 2024. "Optimal Vaccine Subsidies for Epidemic Diseases," The Review of Economics and Statistics, MIT Press, vol. 106(4), pages 895-909, July.
    9. Clemens, Michael A., 2021. "Violence, development, and migration waves: Evidence from Central American child migrant apprehensions," Journal of Urban Economics, Elsevier, vol. 124(C).
    10. Difang Huang & Ying Liang & Boyao Wu & Yanyi Ye, 2025. "Estimating the impact of social distance policy in mitigating COVID-19 spread with factor-based imputation approach," Empirical Economics, Springer, vol. 68(2), pages 585-601, February.
    11. La Torre, Davide & Liuzzi, Danilo & Marsiglio, Simone, 2021. "Epidemics and macroeconomic outcomes: Social distancing intensity and duration," Journal of Mathematical Economics, Elsevier, vol. 93(C).
    12. Mark Gersovitz & Jeffrey S. Hammer, 2004. "The Economical Control of Infectious Diseases," Economic Journal, Royal Economic Society, vol. 114(492), pages 1-27, January.
    13. Miguel Casares & Paul Gomme & Hashmat Khan, 2022. "COVID‐19 pandemic and economic scenarios for Ontario," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 503-539, February.
    14. Glenn Ellison, 2024. "Implications of heterogeneous SIR models for analyses of COVID-19," Review of Economic Design, Springer;Society for Economic Design, vol. 28(4), pages 651-687, December.
    15. S. Bacci & B. Bertaccini, 2021. "Assessment of the University Reputation Through the Analysis of the Student Mobility," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 363-388, August.
    16. Yuki Takara & Shingo Takagi, 2023. "An empirical approach to measure unobserved cultural relations using music trade data," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 47(2), pages 205-245, June.
    17. Rafael Prieto Curiel & Luca Pappalardo & Lorenzo Gabrielli & Steven Richard Bishop, 2018. "Gravity and scaling laws of city to city migration," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-19, July.
    18. Momota, Akira & Tabata, Ken & Futagami, Koichi, 2005. "Infectious disease and preventive behavior in an overlapping generations model," Journal of Economic Dynamics and Control, Elsevier, vol. 29(10), pages 1673-1700, October.
    19. Casey B. Mulligan, 2021. "The Backward Art of Slowing the Spread? Congregation Efficiencies during COVID-19," NBER Working Papers 28737, National Bureau of Economic Research, Inc.
    20. Carolina Coimbra Vieira & Sophie Lohmann & Emilio Zagheni, 2023. "The value of cultural similarity for predicting migration: evidence from digital trace data," MPIDR Working Papers WP-2023-009, Max Planck Institute for Demographic Research, Rostock, Germany.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2008.06051. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.