Bayesian zero-inflated spatio-temporal modelling of scrub typhus data in Korea, 2010-2014

  • Dayun Kang Department of Applied Statistics, Hanyang University, Seoul, Korea, Republic of.
  • Jungsoon Choi | jungsoonchoi@hanyang.ac.kr Department of Mathematics; Research Institute for Natural Sciences, Hanyang University, Seoul, Korea, Republic of.

Abstract

Scrub typhus, a bacterial, febrile disease commonly occurring in the autumn, can easily be cured if diagnosed early. However, it can develop serious complications and even lead to death. For this reason, it is an important issue to find the risk factors and thus be able to prevent outbreaks. We analyzed the monthly scrub typhus data over the entire areas of South Korea from 2010 through 2014. A 2-stage hierarchical framework was considered since weather data are covariates and the scrub typhus data have different spatial resolutions. At the first stage, we obtained the administrative-level estimates for weather data using a spatial model; in the second, we applied a Bayesian zero-inflated spatio-temporal model since the scrub typhus data include excess zero counts. We found that the zero-inflated model considering the spatio-temporal interaction terms improves fitting and prediction performance. This study found that low humidity and a high proportion of elderly people are significantly associated with scrub typhus incidence.

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Published
2018-11-09
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Issue
Section
Original Articles
Keywords:
Scrub typhus, Spatial Kriging, Spatio-temporal model, Zero-inflated Poisson model, Bayesian hierarchical modelling
Statistics
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How to Cite
Kang, D., & Choi, J. (2018). Bayesian zero-inflated spatio-temporal modelling of scrub typhus data in Korea, 2010-2014. Geospatial Health, 13(2). https://doi.org/10.4081/gh.2018.665