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Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression

Investigation article published in One Health

June 24th, 2022

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




Kanankege K., Errecaborde KM., Wiratsudakul A., Wongnak P., Yoopatthanawong C., Thanapongtharm W., Alvarez J. and Perez A.




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Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression





Participants:

University of MinnesotaCollege of Veterinary Medicine. University of Minnesota (UMM).

Mahidol UniversityFaculty of Veterinary Science. Mahidol University.

Universit´e Clermont Auvergne.

VetAgro Sup.

UMR EPIA.

Centre INRAE Val-de-LoireCentre INRAE Val-de-Loire.

Université de Lyon.

Department of Livestock Development. Ministry of Agriculture and Cooperatives.

Universidad ComplutenseCentro de Vigilancia Sanitaria Veterinaria (VISAVET). Universidad Complutense (UCM).

Universidad ComplutenseDepartamento de Sanidad Animal. Facultad de Veterinaria. Universidad Complutense (UCM).







One Health
FACTOR YEAR Q
3.800 2020

ISSN: 2352-7714



TITLE: Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression


JOURNAL: One Health


NUMERACIÓN: 15:100411


AÑO: 2022


PUBLISHER: Elsevier


AUTHORS: Kanankege K., Errecaborde KM., Wiratsudakul A., Wongnak P., Yoopatthanawong C., Thanapongtharm W., Alvarez J. and Perez A.


VISAVET PARTICIPANTS


Julio Álvarez Sánchez

DOI: https://doi.org/10.1016/j.onehlt.2022.100411


CITE THIS PUBLICATION:

Kanankege K., Errecaborde KM., Wiratsudakul A., Wongnak P., Yoopatthanawong C., Thanapongtharm W., Alvarez J. and Perez A. Identifying high-risk areas for dog-mediated rabies using Bayesian spatial regression. One Health. 15:100411. 2022. (A). ISSN: 2352-7714. DOI: 10.1016/j.onehlt.2022.100411