Updated estimation of the Burden of Rabies in Dogs and Humans in Cambodia using Spatial Bayesian Regression Modelling.
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1
Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, United States
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2
Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), Unite Mixte de Recherche, Animal - Sante - Territoires - Risques - Ecosystemes (UMR ASTRE), France
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3
Other, Cambodia
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4
Institut Pasteur du Cambodge, Epidemiology and Public Health Unit, Cambodia
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5
Institut Pasteur du Cambodge, Virology Unit, Cambodia
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6
Institut Pasteur du Cambodge, Cambodia
Based on current estimations, Cambodia has one of the highest rates of rabies in the world, leading to an estimated 800 human deaths per year (Ly et al., 2009). These rabies burden estimates in Cambodia rely on passive surveillance reporting of dog to human bites and human case data using decision tree analysis at the Institut Pasteur du Cambodge (IPC). These yield general national reports which are likely affected by the centralized nature of data collection and reliance on voluntary reporting, leading to potentially biased results with most reported cases and bite injuries coming from areas surrounding and in the capital of Phnom Penh, where IPC is located. Thus, it is likely that, because of the underreporting of rural cases, estimates of rabies are underestimated. In this study, we aim to provide new estimates of the rabies burden in both humans and dogs using methods that better control for uncertainty and data gaps by taking into account the spatial heterogeneity of the data’s distribution. We used a spatial Poisson regression model with a Bayesian framework to model dog-bite injuries in humans as well as canine cases of rabies as a first step towards estimating the number of human cases in Cambodia. The data were collected by IPC, and include systematic individual recording of the approximately 20,000 yearly patients coming to IPC for post-exposure prophylaxis following a bite since 1997 and associated information such as bite severity, biting dog’s behavior, location of residence and demographic factors. Some of these patients occasionally provide the heads of biting dogs, which are tested for rabies. A few hundred heads are tested each year, approximately 50% of which are positive (IPC unpublished data). By using conditional autoregressive structure (CAR) and the travel time from different communes to IPC, we adjusted for the incomplete and unequally distributed nature of reported bite-injury cases caused by biased reporting. We tested various spatial scales for the model, including province, district and commune, to evaluate the impact of the spatial resolution in model outcomes and help choose the most reliable model. Geographical estimates and risk maps are then produced to illustrate high-risk areas and areas suffering from under-reporting and thus lack of access to treatment facilities. Preliminary results confirmed that travel time to IPC is strongly associated with the likeliness of a rabid dog being tested and diagnosed resulting in an underestimation of cases in provinces distant from Phnom Penh. Predictions adjusted for distance and population density confirmed empirical knowledge that certain provinces, such as Kampot, a coastal rural province about 150km South-West of Phnom Penh, have a lower number of reported cases than would be expected on average if surveillance was not centralized. As we move away from the capital, and data become more sparse, uncertainty in our results increases. This modelling study is part of a broader effort to model rabies burden and dynamics ,and simulate diverse interventions in Cambodia to help implement cost-effective rabies prevention and control programs. Providing a more detailed picture of the rabies burden in Cambodia will help pinpoint better where and how to allocate the resources and risk-based strategies, guiding policies to ultimately better prevent and control rabies in Cambodia and other neighboring endemic regions.
Acknowledgements
This work was conducted in part thanks to the fellowship of the graduate student support program at UC Davis.
References
Ly S., Buchy P., Heng N.Y., Ong S., Chhor N., Bourhy H., Vong S. Rabies Situation in Cambodia. PLoS Neglect. Trop. D. 2009 3(9):e511
Keywords:
Lyssavirus,
Mapping,
surveillance,
Disease Prediction,
Asia
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Student oral presentation
Topic:
Spatio-temporal surveillance and modeling approaches
Citation:
Baron
JN,
Chevalier
V,
Ly
S,
Dussart
P,
Fontenille
D and
Martínez-López
B
(2019). Updated estimation of the Burden of Rabies in Dogs and Humans in Cambodia using Spatial Bayesian Regression Modelling..
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00032
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Received:
11 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Dr. Jerome N Baron, Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, Davis, Davis, California, CA 95616-5270, United States, jnbaron@ucdavis.edu