{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T05:47:48Z","timestamp":1784612868811,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2014,12,29]],"date-time":"2014-12-29T00:00:00Z","timestamp":1419811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Satellites contribute significantly to environmental quality and public health. Environmental factors are important indicators for the prediction of disease outbreaks. This study reveals the environmental factors associated with cholera in Zhejiang, a coastal province of China, using both Remote Sensing (RS) and Geographic information System (GIS). The analysis validated the correlation between the indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local cholera magnitude based on a ten-year monthly data from the year 1999 to 2008. Cholera magnitude has been strongly affected by the concurrent variables of SST and SSH, while OCC has a one-month time lag effect. A cholera prediction model has been established based on the sea environmental factors. The results of hot spot analysis showed the local cholera magnitude in counties significantly associated with the estuaries and rivers.<\/jats:p>","DOI":"10.3390\/ijerph120100354","type":"journal-article","created":{"date-parts":[[2014,12,29]],"date-time":"2014-12-29T10:58:34Z","timestamp":1419850714000},"page":"354-370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Identifying Environmental Risk Factors of Cholera in a Coastal Area with Geospatial Technologies"],"prefix":"10.3390","volume":"12","author":[{"given":"Min","family":"Xu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxiang","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duochun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Infectious Disease Prevention and Control, Institute for Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Biao","family":"Kan","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Infectious Disease Prevention and Control, Institute for Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2014,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1080\/2150704X.2013.802097","article-title":"A water marker monitored by satellites to predict seasonal endemic cholera","volume":"4","author":"Antarpreet","year":"2013","journal-title":"Remote Sens. 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