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Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression by Kanankege, Kaushi S.T. (author);Errecaborde, Kaylee Myhre (author);Wiratsudakul, Anuwat (author);Wongnak, Phrutsamon (author);Yoopatthanawong, Chakchalat (author);Thanapongtharm, Weerapong (author);Alvarez, Julio (author);Perez, Andres (author) is a Medicine article available to read on EtoBox.

What is Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression about?

Despite ongoing control efforts, rabies remains an endemic zoonotic disease in many countries. Determining high-risk areas and the space-time patterns of rabies spread, as it relates to epidemiologically important factors, can support policymakers and program managers alike to develop evidence-based targeted surveillance and control programs. In this One Health approach which selected Thailand as the example site, the location-based risk of contracting dog-mediated rabies by both human and animal populations was quantified using a Bayesian spatial regression model. Specifically, a conditional autoregressive (CAR) Bayesian zero-inflated Poisson (ZIP) regression was fitted to the reported human and animal rabies case counts of each district, from the 2012–2017 period. The human population was used as an offset. The epidemiologically important factors hypothesized as risk modifiers and therefore tested as predictors included: number of dog bites/attacks, the population of dogs and cats, number of Buddhist temples, garbage dumps, animal vaccination, post-exposure prophylaxis, poverty, and shared administrative borders. Disparate sources of data were used to improve the estimated associ

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Author

Kanankege, Kaushi S.T. (author);Errecaborde, Kaylee Myhre (author);Wiratsudakul, Anuwat (author);Wongnak, Phrutsamon (author);Yoopatthanawong, Chakchalat (author);Thanapongtharm, Weerapong (author);Alvarez, Julio (author);Perez, Andres (author)

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Medicine (Health Sciences)