{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T05:48:12Z","timestamp":1784612892843,"version":"3.55.0"},"reference-count":30,"publisher":"Cambridge University Press (CUP)","issue":"5","license":[{"start":{"date-parts":[[2015,10,14]],"date-time":"2015-10-14T00:00:00Z","timestamp":1444780800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["journals.cambridge.org"],"crossmark-restriction":true},"short-container-title":["Epidemiol. Infect."],"published-print":{"date-parts":[[2016,4]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>Cholera is one of a number of infectious diseases that appears to be influenced by climate, geography and other natural environments. This study analysed the environmental factors of the spatial distribution of cholera in China. It shows that temperature, precipitation, elevation, and distance to the coastline have significant impact on the distribution of cholera. It also reveals the oceanic environmental factors associated with cholera in Zhejiang, which is a coastal province of China, using both remote sensing (RS) and geographical information systems (GIS). The analysis has validated the correlation between indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local number of cholera cases based on 8-year monthly data from 2001 to 2008. The results show the number of cholera cases has been strongly affected by the variables of SST, SSH and OCC. Utilizing this information, a cholera prediction model has been established based on the oceanic and climatic environmental factors. The model indicates that RS and GIS have great potential for designing an early warning system for cholera.<\/jats:p>","DOI":"10.1017\/s095026881500223x","type":"journal-article","created":{"date-parts":[[2015,10,14]],"date-time":"2015-10-14T06:37:38Z","timestamp":1444804658000},"page":"940-951","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":15,"title":["Environmental factor analysis of cholera in China using remote sensing and geographical information systems"],"prefix":"10.1017","volume":"144","author":[{"given":"M.","family":"XU","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. X.","family":"CAO","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D. 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