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

Jiao Wang, Peng Jia, Diego F. Cuadros, Min Xu, Xianliang Wang, Weidong Guo, Boris A. Portnov, Yuhai Bao, Yushan Chang, Genxin Song, Nan Chen, Alfred Stein

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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.
Original languageEnglish
Pages (from-to)1018
JournalRemote sensing
Volume9
Issue number10
DOIs
Publication statusPublished - 2017

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brucellosis
infectious disease
artificial neural network
remote sensing
climate
environmental modeling
vegetation index
train
satellite data
land surface
surface temperature

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

Wang, Jiao ; Jia, Peng ; Cuadros, Diego F. ; Xu, Min ; Wang, Xianliang ; Guo, Weidong ; Portnov, Boris A. ; Bao, Yuhai ; Chang, Yushan ; Song, Genxin ; Chen, Nan ; Stein, Alfred. / A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks : A Case Study of Human Brucellosis. In: Remote sensing. 2017 ; Vol. 9, No. 10. pp. 1018.
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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.",
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A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks : A Case Study of Human Brucellosis. / Wang, Jiao; Jia, Peng; Cuadros, Diego F.; Xu, Min; Wang, Xianliang; Guo, Weidong; Portnov, Boris A.; Bao, Yuhai; Chang, Yushan; Song, Genxin; Chen, Nan; Stein, Alfred.

In: Remote sensing, Vol. 9, No. 10, 2017, p. 1018.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Xu, Min

AU - Wang, Xianliang

AU - Guo, Weidong

AU - Portnov, Boris A.

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AU - Song, Genxin

AU - Chen, Nan

AU - Stein, Alfred

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N2 - 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.

AB - 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.

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