A linear-time algorithm was recently published (International Conference Proceedings ofPacific Graphics' 94/CADDM' 94, August 26-29 , 1994 , Beijing , China) for computing the convexhull of a simple polygon. I...A linear-time algorithm was recently published (International Conference Proceedings ofPacific Graphics' 94/CADDM' 94, August 26-29 , 1994 , Beijing , China) for computing the convexhull of a simple polygon. In this note we present a counter-example to that algorithm by exhibiting afamily of polygons for which the algorithm discards vertices that are on the convex hull.展开更多
Background Vector-borne diseases(VBDs)are important contributors to the global burden of infectious diseases due to their epidemic potential,which can result in signifcant population and economic impacts.Oropouche fev...Background Vector-borne diseases(VBDs)are important contributors to the global burden of infectious diseases due to their epidemic potential,which can result in signifcant 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.Methods To 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 phe‑nology.Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas.Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of diferent parameters such as diferent 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 out‑breaks have occurred under climatic conditions outside those where most transmission events occur.The distribu‑tion models also revealed that landscape variation,expressed as vegetation loss,is linked to OROV outbreaks.Conclusions Hotspots 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 understand‑ing exists on their sylvatic cycles.OROV transmission risk maps can be used to improve surveillance,investigate OROV ecology and epidemiology,and inform early detection.展开更多
文摘A linear-time algorithm was recently published (International Conference Proceedings ofPacific Graphics' 94/CADDM' 94, August 26-29 , 1994 , Beijing , China) for computing the convexhull of a simple polygon. In this note we present a counter-example to that algorithm by exhibiting afamily of polygons for which the algorithm discards vertices that are on the convex hull.
文摘Background Vector-borne diseases(VBDs)are important contributors to the global burden of infectious diseases due to their epidemic potential,which can result in signifcant 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.Methods To 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 phe‑nology.Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas.Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of diferent parameters such as diferent 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 out‑breaks have occurred under climatic conditions outside those where most transmission events occur.The distribu‑tion models also revealed that landscape variation,expressed as vegetation loss,is linked to OROV outbreaks.Conclusions Hotspots 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 understand‑ing exists on their sylvatic cycles.OROV transmission risk maps can be used to improve surveillance,investigate OROV ecology and epidemiology,and inform early detection.
