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Abstract BackgroundCache Valley virus (CVV) is an understudiedOrthobunyaviruswith a high spillover transmission potential due to its wide geographical distribution and large number of associated hosts and vectors. Although CVV is known to be widely distributed throughout North America, no studies have explored its geography or employed computational methods to explore the mammal and mosquito species likely participating in the CVV sylvatic cycle. MethodsWe used a literature review and online databases to compile locality data for CVV and its potential vectors and hosts. We linked location data points with climatic data via ecological niche modeling to estimate the geographical range of CVV and hotspots of transmission risk. We used background similarity tests to identify likely CVV mosquito vectors and mammal hosts to detect ecological signals from CVV sylvatic transmission. ResultsCVV distribution maps revealed a widespread potential viral occurrence throughout North America. Ecological niche models identified areas with climate, vectors, and hosts suitable to maintain CVV transmission. Our background similarity tests identifiedAedes vexans,Culiseta inornata, andCulex tarsalisas the most likely vectors andOdocoileus virginianus(white-tailed deer) as the most likely host sustaining sylvatic transmission. ConclusionsCVV has a continental-level, widespread transmission potential. Large areas of North America have suitable climate, vectors, and hosts for CVV emergence, establishment, and spread. We identified geographical hotspots that have no confirmed CVV reports to date and, in view of CVV misdiagnosis or underreporting, can guide future surveillance to specific localities and species. Graphical Abstract 

Abstract 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 those where 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.