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Spatio-temporal modeling of infectious disease dynamics

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  • Sifat Sharmin
  • Md. Israt Rayhan

Abstract

A stochastic model, which is well suited to capture space--time dependence of an infectious disease, was employed in this study to describe the underlying spatial and temporal pattern of measles in Barisal Division, Bangladesh. The model has two components: an endemic component and an epidemic component; weights are used in the epidemic component for better accounting of the disease spread into different geographical regions. We illustrate our findings using a data set of monthly measles counts in the six districts of Barisal, from January 2000 to August 2009, collected from the Expanded Program on Immunization, Bangladesh. The negative binomial model with both the seasonal and autoregressive components was found to be suitable for capturing space--time dependence of measles in Barisal. Analyses were done using general optimization routines, which provided the maximum likelihood estimates with the corresponding standard errors.

Suggested Citation

  • Sifat Sharmin & Md. Israt Rayhan, 2012. "Spatio-temporal modeling of infectious disease dynamics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 875-882, September.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:875-882
    DOI: 10.1080/02664763.2011.624593
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    References listed on IDEAS

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    1. Alexander Morton & Bärbel F. Finkenstädt, 2005. "Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 575-594, June.
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    3. N. G. Becker & T. Britton, 1999. "Statistical studies of infectious disease incidence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 287-307, April.
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