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Detecting a trend change in cross-border epidemic transmission

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  • Maeno, Yoshiharu

Abstract

A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases reported in multiple geographical regions. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:457:y:2016:i:c:p:73-81
    DOI: 10.1016/j.physa.2016.03.039
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    References listed on IDEAS

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    1. Maeno, Yoshiharu, 2011. "Discovery of a missing disease spreader," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3412-3426.
    2. Walker, David M. & Allingham, David & Lee, Heung Wing Joseph & Small, Michael, 2010. "Parameter inference in small world network disease models with approximate Bayesian Computational methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 540-548.
    3. Steffen Unkel & C. Paddy Farrington & Paul H. Garthwaite & Chris Robertson & Nick Andrews, 2012. "Statistical methods for the prospective detection of infectious disease outbreaks: a review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 49-82, January.
    4. 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.
    5. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    6. Maeno, Yoshiharu, 2010. "Discovering network behind infectious disease outbreak," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4755-4768.
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    Cited by:

    1. Aminullah, Erman & Erman, Erwiza, 2021. "Policy innovation and emergence of innovative health technology: The system dynamics modelling of early COVID-19 handling in Indonesia," Technology in Society, Elsevier, vol. 66(C).

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