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Transmission dynamics of re-emerging rabies in domestic dogs of rural China

Despite ongoing efforts to control transmission, rabies prevention remains a challenge in many developing countries, especially in rural areas of China where re-emerging rabies is under-reported due to a lack of sustained animal surveillance. By taking advantage of detailed genomic and epidemiologic...

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Detalles Bibliográficos
Autores principales: Tian, Huaiyu, Feng, Yun, Vrancken, Bram, Cazelles, Bernard, Tan, Hua, Gill, Mandev S., Yang, Qiqi, Li, Yidan, Yang, Weihong, Zhang, Yuzhen, Zhang, Yunzhi, Lemey, Philippe, Pybus, Oliver G., Stenseth, Nils Chr., Zhang, Hailin, Dellicour, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283347/
https://www.ncbi.nlm.nih.gov/pubmed/30521641
http://dx.doi.org/10.1371/journal.ppat.1007392
Descripción
Sumario:Despite ongoing efforts to control transmission, rabies prevention remains a challenge in many developing countries, especially in rural areas of China where re-emerging rabies is under-reported due to a lack of sustained animal surveillance. By taking advantage of detailed genomic and epidemiological data for the re-emerging rabies outbreak in Yunnan Province, China, collected between 1999 and 2015, we reconstruct the demographic and dispersal history of domestic dog rabies virus (RABV) as well as the dynamics of dog-to-dog and dog-to-human transmission. Phylogeographic analyses reveal a lower diffusion coefficient than previously estimated for dog RABV dissemination in northern Africa. Furthermore, epidemiological analyses reveal transmission rates between dogs, as well as between dogs and humans, lower than estimates for Africa. Finally, we show that reconstructed epidemic history of RABV among dogs and the dynamics of rabid dogs are consistent with the recorded human rabies cases. This work illustrates the benefits of combining phylogeographic and epidemic modelling approaches for uncovering the spatiotemporal dynamics of zoonotic diseases, with both approaches providing estimates of key epidemiological parameters.