Publication: Bayesian spatial modeling for the joint analysis of zoonosis between human and animal populations
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Issued Date
2018-12-01
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ISSN
22116753
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2-s2.0-85053728424
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Mahidol University
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SCOPUS
Bibliographic Citation
Spatial Statistics. Vol.28, (2018), 8-20
Suggested Citation
Andrew B. Lawson, Chawarat Rotejanaprasert Bayesian spatial modeling for the joint analysis of zoonosis between human and animal populations. Spatial Statistics. Vol.28, (2018), 8-20. doi:10.1016/j.spasta.2018.08.001 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/45731
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Title
Bayesian spatial modeling for the joint analysis of zoonosis between human and animal populations
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Abstract
© 2018 Elsevier B.V. Human case monitoring is important for investigating disease burden; however, using human cases by themselves may not be sufficient for evaluating outbreaks. Enhanced surveillance of human cases should be considered, especially when other epidemiological indexes suggest that an outbreak is suspected or anticipated. In this paper we review the requirements for the effective modeling of both human and animal disease occurrence. We advocate for the use of joint models for these disease hosts, based on the need for flexible specification of model components and the flexible specification of correlation between animal and human disease. In the special case of infective diseases we advocate the use of both direct dependencies (via compartmental models) and the use of shared effects to allow the confounder effects that are common to be modeled. Case studies of integrated surveillance are provided focused on Tularemia human incidence with rodent population data from Finnish health care districts and the multivariate monitoring of West Nile virus activity in California, USA.