{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T04:13:03Z","timestamp":1773288783184,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,30]],"date-time":"2017-09-30T00:00:00Z","timestamp":1506729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education","award":["JOF201704"],"award-info":[{"award-number":["JOF201704"]}]},{"name":"Key Laboratory of Spatial Data Mining &amp; Information Sharing of Ministry of Education, Fuzhou University","award":["2017LSDMIS08"],"award-info":[{"award-number":["2017LSDMIS08"]}]},{"name":"Key Laboratory for National Geographic State Monitoring (National Administration of Surveying, Mapping and Geoinformation)","award":["2015NGCM"],"award-info":[{"award-number":["2015NGCM"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing technologies can accurately capture environmental characteristics, and together with environmental modeling approaches, help to predict climate-sensitive infectious disease outbreaks. Brucellosis remains rampant worldwide in both domesticated animals and humans. This study used human brucellosis (HB) as a test case to identify important environmental determinants of the disease and predict its outbreaks. A novel artificial neural network (ANN) model was developed, using annual county-level numbers of HB cases and data on 37 environmental variables, potentially associated with HB in Inner Mongolia, China. Data from 2006 to 2008 were used to train, validate and test the model, while data for 2009\u20132010 were used to assess the model\u2019s performance. The Enhanced Vegetation Index was identified as the most important predictor of HB incidence, followed by land surface temperature and other temperature- and precipitation-related variables. The suitable ecological niche of HB was modeled based on these predictors. Model estimates were found to be in good agreement with reported numbers of HB cases in both the model development and assessment phases. The study suggests that HB outbreaks may be predicted, with a reasonable degree of accuracy, using the ANN model and environmental variables obtained from satellite data. The study deepened the understanding of environmental determinants of HB and advanced the methodology for prediction of climate-sensitive infectious disease outbreaks.<\/jats:p>","DOI":"10.3390\/rs9101018","type":"journal-article","created":{"date-parts":[[2017,10,2]],"date-time":"2017-10-02T13:10:05Z","timestamp":1506949805000},"page":"1018","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks: A Case Study of Human Brucellosis"],"prefix":"10.3390","volume":"9","author":[{"given":"Jiao","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions,Ministry of Education, Henan University, Kaifeng 475004, China"},{"name":"National Institute of Environmental Health, Chinese Center for Disease Control and Prevention,Beijing 100021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0110-3637","authenticated-orcid":false,"given":"Peng","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation,University of Twente, Enschede 7500, The Netherlands"}]},{"given":"Diego F.","family":"Cuadros","sequence":"additional","affiliation":[{"name":"Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH 45221, USA"},{"name":"Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, OH 45221, USA"}]},{"given":"Min","family":"Xu","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"}]},{"given":"Xianliang","family":"Wang","sequence":"additional","affiliation":[{"name":"National Institute of Environmental Health, Chinese Center for Disease Control and Prevention,Beijing 100021, China"}]},{"given":"Weidong","family":"Guo","sequence":"additional","affiliation":[{"name":"Inner Mongolia Center for Disease Control and Prevention, Hohhot 010031, China"}]},{"given":"Boris A.","family":"Portnov","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Environmental Management, Faculty of Management, University of Haifa, Haifa 3498838, Israel"}]},{"given":"Yuhai","family":"Bao","sequence":"additional","affiliation":[{"name":"Inner Mongolian Key Laboratory of Remote Sensing and GIS, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Yushan","family":"Chang","sequence":"additional","affiliation":[{"name":"Inner Mongolian Key Laboratory of Remote Sensing and GIS, Inner Mongolia Normal University, Hohhot 010022, China"}]},{"given":"Genxin","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions,Ministry of Education, Henan University, Kaifeng 475004, China"}]},{"given":"Nan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9456-1233","authenticated-orcid":false,"given":"Alfred","family":"Stein","sequence":"additional","affiliation":[{"name":"Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation,University of Twente, Enschede 7500, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jia, P., and Joyner, A. 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