Transmission risk of Oropouche fever across the Americas

Infectious Diseases of Poverty

Research Article

Daniel Romero-Alvarez¹  Albert J. Auguste²  Luis E. Escobar³  Sara Y. Del Valle⁴  Carrie A. Manore⁵ 

[1] Biodiversity Institute and Department of Ecology & Evolutionary Biology, University of Kansas, 66044, Lawrence, KS, USA;Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA;OneHealth Research Group, Facultad de Medicina, Universidad de las Américas, Quito, Ecuador;Center for Emerging, Zoonotic, and Arthropod-Borne Pathogens, Virginia Tech, 24061, Blacksburg, VA, USA;Department of Entomology, Fralin Life Science Institute, College of Agriculture and Life Sciences, Virginia Tech, 24061, Blacksburg, VA, USA;Department of Fish and Wildlife Conservation, Virginia Tech, 24061, Blacksburg, VA, USA;Center for Emerging, Zoonotic, and Arthropod-Borne Pathogens, Virginia Tech, 24061, Blacksburg, VA, USA;Information Systems and Modeling (A-1), Los Alamos National Laboratory, Los Alamos, NM, USA;Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, NM, USA;

关键词: Oropouche virus;    Oropouche fever;    Spatial modeling;    Hypervolumes;    Distribution modeling;    Risk mapping;    One-class support vector machines;    Convex-hulls;   

DOI  :  10.1186/s40249-023-01091-2

 received in 2022-11-09, accepted in 2023-04-04,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundVector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance.MethodsTo better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas.ResultsModels based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside thosewhere most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks.ConclusionsHotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.

【 授权许可】

CC BY   
© The Author(s) 2023

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