Predictive Time Series Analysis Linking Bengal Cholera with Terrestrial Water Storage Measured from Gravity Recovery and Climate Experiment Sensors

Antarpreet Jutla Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia; Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, Rhode Island; Department of Civil and Environmental Engineering, Portland State University, Portland, Oregon; Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

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Ali Akanda Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia; Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, Rhode Island; Department of Civil and Environmental Engineering, Portland State University, Portland, Oregon; Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

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Avinash Unnikrishnan Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia; Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, Rhode Island; Department of Civil and Environmental Engineering, Portland State University, Portland, Oregon; Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

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Anwar Huq Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia; Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, Rhode Island; Department of Civil and Environmental Engineering, Portland State University, Portland, Oregon; Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

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Rita Colwell Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia; Department of Civil and Environmental Engineering, University of Rhode Island, Kingston, Rhode Island; Department of Civil and Environmental Engineering, Portland State University, Portland, Oregon; Maryland Pathogen Research Institute, University of Maryland, College Park, Maryland; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

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DOI:
https://doi.org/10.4269/ajtmh.14-0648
Volume/Issue:
Volume 93: Issue 6
Page(s):
1179–1186
Restricted access
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Outbreaks of diarrheal diseases, including cholera, are related to floods and droughts in regions where water and sanitation infrastructure are inadequate or insufficient. However, availability of data on water scarcity and abundance in transnational basins, are a prerequisite for developing cholera forecasting systems. With more than a decade of terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment, conditions favorable for predicting cholera occurrence may now be determined. We explored lead–lag relationships between TWS in the Ganges–Brahmaputra–Meghna basin and endemic cholera in Bangladesh. Since bimodal seasonal peaks in cholera in Bangladesh occur during spring and autumn seasons, two separate logistical models between TWS and disease time series (2002–2010) were developed. TWS representing water availability showed an asymmetrical, strong association with cholera prevalence in the spring (τ = −0.53; P < 0.001) and autumn (τ = 0.45; P < 0.001) up to 6 months in advance. One unit (centimeter of water) decrease in water availability in the basin increased odds of above normal cholera by 24% (confidence interval [CI] = 20–31%; P < 0.05) in the spring, while an increase in regional water by 1 unit, through floods, increased odds of above average cholera in the autumn by 29% (CI = 22–33%; P < 0.05).

Author Notes

* Address correspondence to Antarpreet Jutla or Rita Colwell, Department of Civil and Environmental Engineering, West Virginia University, P.O. 6013, Morgantown, WV 26506. E-mails: asjutla@mail.wvu.edu or rcolwell@umiacs.umd.edu

Authors' addresses: Antarpreet Jutla, Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV, E-mail: asjutla@mail.wvu.edu. Ali Shafqat Akanda, Department of Civil and Environmental Engineering, Tufts University, Medford, MA, E-mail: akanda@egr.uri.edu. Avinash Unnikrishnan, Department of Civil and Environmental Engineering, Portland State University, Portland, OR, E-mail: avinash.unnikrishnan@mail.wvu.edu. Anwar Huq, Maryland Pathogen Research Institute, University of Maryland, College Park, MD, E-mail: huqanwar@gmail.com. Rita Colwell, Maryland Pathogen Research Institute, University of Maryland, College Park, MD, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, and Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mail: rcolwell@umiacs.umd.edu.

Copyright:
© The American Society of Tropical Medicine and Hygiene 2015
Received:
17 Oct 2014
|
Accepted:
08 Sep 2015
|
Published Online:
09 Dec 2015
Cover The American Journal of Tropical Medicine and Hygiene
The American Journal of Tropical Medicine and Hygiene
Print ISSN:
0002-9637
Online ISSN:
1476-1645
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