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Remote Sens. 2017, 9(10), 1018; https://doi.org/10.3390/rs9101018

A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks: A Case Study of Human Brucellosis

1
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions,Ministry of Education, Henan University, Kaifeng 475004, China
2
National Institute of Environmental Health, Chinese Center for Disease Control and Prevention,Beijing 100021, China
3
Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation,University of Twente, Enschede 7500, The Netherlands
4
Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH 45221, USA
5
Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, OH 45221, USA
6
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
7
Inner Mongolia Center for Disease Control and Prevention, Hohhot 010031, China
8
Department of Natural Resources and Environmental Management, Faculty of Management, University of Haifa, Haifa 3498838, Israel
9
Inner Mongolian Key Laboratory of Remote Sensing and GIS, Inner Mongolia Normal University, Hohhot 010022, China
10
Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350003, China
These two authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 30 June 2017 / Revised: 8 August 2017 / Accepted: 22 September 2017 / Published: 30 September 2017
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Abstract

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 2009–2010 were used to assess the model’s 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. View Full-Text
Keywords: infectious disease; climate-sensitive; artificial neural network (ANN); remote sensing; brucellosis; disease outbreak prediction; environmental health; Inner Mongolia infectious disease; climate-sensitive; artificial neural network (ANN); remote sensing; brucellosis; disease outbreak prediction; environmental health; Inner Mongolia
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, J.; Jia, P.; Cuadros, D.F.; Xu, M.; Wang, X.; Guo, W.; Portnov, B.A.; Bao, Y.; Chang, Y.; Song, G.; Chen, N.; Stein, A. A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks: A Case Study of Human Brucellosis. Remote Sens. 2017, 9, 1018.

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