Spatial scanning is done for two regions in Chinese Mainland,where displayed a denseprecursory network during 1994～1998.The two regions are the mid-southern segment of theNorth-south seismic belt(20°～35°N,...Spatial scanning is done for two regions in Chinese Mainland,where displayed a denseprecursory network during 1994～1998.The two regions are the mid-southern segment of theNorth-south seismic belt(20°～35°N,95°～110°E)and North China(36°～42°N,110°～120°E).We took 0.5°×0.5°as a spatial window with a step of 0.25°and 4 months as atemporal window with a step of 1 month.For the two regions,the anomaly density is scannedfrom 1994 and 1995 respectively in the two regions.The precursory anomalies are all fromthe Division of Seismic Trend in China and the Division of Seismic Trend in the Capital Area,Center for Analysis and Prediction,China Seismological Bureau.A seismogenic tectonicmodel is introduced to explain the scanning results.In the model,the frictional strength ofthe focal sources is distributed randomly.After the boundary plate motion rate and all othergeological parameters are given,the stress of the sources in the system changesinhomogenously due to the variation of the frictional展开更多
Background Cutaneous leishmaniasis(CL)is a wide-reaching infection of major public health concern.Iran is one of the six most endemic countries in the world.This study aims to provide a spatiotemporal visualization of...Background Cutaneous leishmaniasis(CL)is a wide-reaching infection of major public health concern.Iran is one of the six most endemic countries in the world.This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020,detecting high-risk zones,while also noting the movement of high-risk clusters.Methods On the basis of clinical observations and parasitological tests,data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education.Utilizing spatial scan statistics,we investigated the disease’s purely temporal,purely spatial,spatial variation in temporal trends and spatiotemporal patterns.At P=0.05 level,the null hypothesis was rejected in every instance.Results In general,the number of new CL cases decreased over the course of the 9-year research period.From 2011 to 2020,a regular seasonal pattern,with peaks in the fall and troughs in the spring,was found.The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country[relative risk(RR)=2.24,P<0.001)].In terms of location,six signifcant high-risk CL clusters covering 40.6%of the total area of the country were observed,with the RR ranging from 1.87 to 9.69.In addition,spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency.Finally,fve space-time clusters were found.The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period afecting many regions of the country.Conclusions Our study has revealed signifcant regional,temporal,and spatiotemporal patterns of CL distribution in Iran.Over the years,there have been multiple shifts in spatiotemporal clusters,encompassing many diferent parts of the country from 2011 to 2020.The results reveal the formation of clusters across counties that cover certain parts of provinces,indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries.Such analyses,at a fner geographical scale,such as county level,might provide more precise results than analyses at the scale of the province.展开更多
基金This project was sponsored by the China Seismological Bureau(95-04-03-03-02)
文摘Spatial scanning is done for two regions in Chinese Mainland,where displayed a denseprecursory network during 1994～1998.The two regions are the mid-southern segment of theNorth-south seismic belt(20°～35°N,95°～110°E)and North China(36°～42°N,110°～120°E).We took 0.5°×0.5°as a spatial window with a step of 0.25°and 4 months as atemporal window with a step of 1 month.For the two regions,the anomaly density is scannedfrom 1994 and 1995 respectively in the two regions.The precursory anomalies are all fromthe Division of Seismic Trend in China and the Division of Seismic Trend in the Capital Area,Center for Analysis and Prediction,China Seismological Bureau.A seismogenic tectonicmodel is introduced to explain the scanning results.In the model,the frictional strength ofthe focal sources is distributed randomly.After the boundary plate motion rate and all othergeological parameters are given,the stress of the sources in the system changesinhomogenously due to the variation of the frictional
文摘Background Cutaneous leishmaniasis(CL)is a wide-reaching infection of major public health concern.Iran is one of the six most endemic countries in the world.This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020,detecting high-risk zones,while also noting the movement of high-risk clusters.Methods On the basis of clinical observations and parasitological tests,data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education.Utilizing spatial scan statistics,we investigated the disease’s purely temporal,purely spatial,spatial variation in temporal trends and spatiotemporal patterns.At P=0.05 level,the null hypothesis was rejected in every instance.Results In general,the number of new CL cases decreased over the course of the 9-year research period.From 2011 to 2020,a regular seasonal pattern,with peaks in the fall and troughs in the spring,was found.The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country[relative risk(RR)=2.24,P<0.001)].In terms of location,six signifcant high-risk CL clusters covering 40.6%of the total area of the country were observed,with the RR ranging from 1.87 to 9.69.In addition,spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency.Finally,fve space-time clusters were found.The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period afecting many regions of the country.Conclusions Our study has revealed signifcant regional,temporal,and spatiotemporal patterns of CL distribution in Iran.Over the years,there have been multiple shifts in spatiotemporal clusters,encompassing many diferent parts of the country from 2011 to 2020.The results reveal the formation of clusters across counties that cover certain parts of provinces,indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries.Such analyses,at a fner geographical scale,such as county level,might provide more precise results than analyses at the scale of the province.
