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

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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number100411
JournalOne Health
Volume15
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
This project was funded by the Center for Global Health and Social Responsibility of the University of Minnesota. We would like to acknowledge Thailand's Department of Livestock Development, Ministry of Agriculture and Cooperatives and Department of Disease Control, Ministry of Public Health, and Food and Agriculture Organization of the United Nations (FAO) for their support providing information and technical consultation to the project. We extend the acknowledgement to the Centers for Disease Control and Prevention for their feedback on the manuscript.

Publisher Copyright:
© 2022 The Authors

Keywords

  • Conditional autoregression
  • Disease mapping
  • One Health
  • Risk regionalization
  • Spatial epidemiology
  • Stray dogs
  • Zero-inflated

PubMed: MeSH publication types

  • Journal Article

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