Identifying opportunities to improve the effectiveness in rabies control in Thailand using geographical analysis

Field: Veterinary (Health Sciences)

Event Abstract Back to Event Identifying opportunities to improve the effectiveness in rabies control in Thailand using geographical analysis Kaushi S. Kanankege1*, Anuwat Wiratsudakul2, Ong-Orn Prasarnphanich3, Phrusamon Wongnak4, Chakchalat Yoopatthanawon4, Julio Alvarez5, Kaylee Errecaborde1 and Andres M. Perez1 1 University of Minnesota Twin Cities, United States 2 Mahidol University, Thailand 3 Thailand Ministry of Public Health-U.S. Ceneter for Disease Control and Prevention Collaboration, Thailand 4 Faculty of Veterinary Science, Mahidol University, Thailand 5 Centro de Vigilancia Sanitaria Veterinaria (VISAVET) and Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense, Spain Despite immense efforts, rabies remains an endemic and neglected zoonotic disease in Asia and Africa, including Thailand. To evaluate and improve the effectiveness of control programs, it is imperative for policymakers and program managers alike to understand the spatiotemporal patterns of rabies spread in relation to underlying risk factors. Here, we generated rabies risk maps for Thailand using Bayesian spatial regression. Major risk factors associated with dog-mediated rabies were identified through published literature, and the relevant data were collected using publicly available and government institutional data sources. A linear regression model was used to investigate the association between the potential risk of rabies and available covariates at the sub-district level. To represent the potential risk of rabies at the sub-district level, both human and animal rabies cases and the number of dog bites were used to estimate the risk. In addition to the covariates, random effects pertaining to spatial dependence were included in the model using a conditional autoregressive model that accounted for the spatial autocorrelation with adjacent sub-districts. Preliminary results suggest that the number of Buddhist temples, human population density, and unowned dogs and cat population were significantly associated with the risk of rabies The number of open garbage dumps, which provide a site for food for the free-roaming animals, was not statistically significant (p<0.05). The fitted values were mapped and classified into five based on natural breaks. Sub-districts with classes three and above were considered to be at high risk (1412/7768; 18.18%). In subsequent steps, data available at both sub-district and provincial level, including the anti-rabies vaccination of dogs and the human post-exposure prophylaxis (PEP) usage, will be incorporated following a nested modeling approach. The risk maps may be useful to improve the current dissemination of dog vaccination, at the sub-district level, in Thailand. Acknowledgements We acknowledge the Center for Global Health and Social Responsibility of the University of Minnesota for funding this research. Keywords: Conditional auto regressive model, One Health, Thailand, Risk maps, Rabies - epidemiology Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019. Presentation Type: Regular oral presentation Topic: Spatio-temporal surveillance and modeling approaches Citation: Kanankege KS, Wiratsudakul A, Prasarnphanich O, Wongnak P, Yoopatthanawon C, Alvarez J, Errecaborde K and Perez AM (2019). Identifying opportunities to improve the effectiveness in rabies control in Thailand using geographical analysis. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00028 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 Jun 2019; Published Online: 27 Sep 2019. * Correspondence: Mx. Kaushi S Kanankege, University of Minnesota Twin Cities, St. Paul, United States, kanan009@umn.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Kaushi S Kanankege Anuwat Wiratsudakul Ong-Orn Prasarnphanich Phrusamon Wongnak Chakchalat Yoopatthanawon Julio Alvarez Kaylee Errecaborde Andres M Perez Google Kaushi S Kanankege Anuwat Wiratsudakul Ong-Orn Prasarnphanich Phrusamon Wongnak Chakchalat Yoopatthanawon Julio Alvarez Kaylee Errecaborde Andres M Perez Google Scholar Kaushi S Kanankege Anuwat Wiratsudakul Ong-Orn Prasarnphanich Phrusamon Wongnak Chakchalat Yoopatthanawon Julio Alvarez Kaylee Errecaborde Andres M Perez PubMed Kaushi S Kanankege Anuwat Wiratsudakul Ong-Orn Prasarnphanich Phrusamon Wongnak Chakchalat Yoopatthanawon Julio Alvarez Kaylee Errecaborde Andres M Perez Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.