RT Journal Article
T1 Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression
A1 Kanankege, Kaushi S.T.
A1 Errecaborde, Kaylee Myhre
A1 Wiratsudakul, Anuwat
A1 Wongnak, Phrutsamon
A1 Yoopatthanawong, Chakchalat
A1 Thanapongtharm, Weerapong
A1 Álvarez Sánchez, Julio
A1 Perez, Andres
AB 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 associations and predictions. Model performance was assessed using cross-validation. Results suggested that accounting for the association between human and animal rabies with number of dog bites/attacks, number of owned and un-owned dogs; shared country borders, number of Buddhist temples, poverty levels, and accounting for spatial dependence between districts, may help to predict the risk districts for dog-mediated rabies in Thailand. The fitted values of the spatial regression were mapped to illustrate the risk of dog-mediated rabies. The cross-validation indicated an adequate performance of the spatial regression model (AUC = 0.81), suggesting that had this spatial regression approach been used to identify districts at risk in 2015, the cases reported in 2016/17 would have been predicted with model sensitivity and specificity of 0.71 and 0.80, respectively. While active surveillance is ideal, this approach of using multiple data sources to improve risk estimation may inform current rabies surveillance and control efforts including determining rabies-free zones, and the roll-out of human post-exposure prophylaxis and anti-rabies vaccines for animals in determining high-risk areas
PB Elsevier
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/134155
UL https://hdl.handle.net/20.500.14352/134155
LA eng
NO Kanankege, K. S. T., Errecaborde, K. M., Wiratsudakul, A., Wongnak, P., Yoopatthanawong, C., Thanapongtharm, W., Alvarez, J., & Perez, A. (2022). Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression. One health (Amsterdam, Netherlands), 15, 100411. https://doi.org/10.1016/j.onehlt.2022.100411
NO Credit authorship contribution statementKaushi S.T. Kanankege: Conceptualization, Data curation, Investigation, Methodology, Project administration, Visualization, Software, Writing – original draft. Kaylee Myhre Errecaborde: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing. Anuwat Wiratsudakul: Funding acquisition, Data curation, Project administration, Writing – review & editing. Phrutsamon Wongnak: Data curation, Writing – review & editing. Chakchalat Yoopatthanawong: Data curation. Weerapong Thanapongtharm: Data curation, Resources, Writing – review & editing. Julio Alvarez: Methodology, Writing – review & editing. Andres Perez: Supervision, Resources, Methodology, Writing – review & editing.
NO University of Minnesota
DS Docta Complutense
RD 15 abr 2026