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

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  • 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
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    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. 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.
    8. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    9. 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.
    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.
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    Cited by:

    1. Callaway, Brantly & Li, Tong, 2023. "Policy evaluation during a pandemic," Journal of Econometrics, Elsevier, vol. 236(1).

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