Evidence map›Paper›PMID 26526921›Full record

ArticleThe American journal of tropical medicine and hygiene2015

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

Antarpreet Jutla, Ali Akanda, Avinash Unnikrishnan, Anwar Huq, Rita Colwell

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The American journal of tropical medicine and hygiene, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Climate change andProceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Antarpreet JutlaDepartment 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 asjutla@mail.wvu.edu rcolwell@umiacs.umd.edu.
Ali AkandaDepartment 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.
Avinash UnnikrishnanDepartment 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.
Anwar HuqDepartment 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.
Rita ColwellDepartment 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 asjutla@mail.wvu.edu rcolwell@umiacs.umd.edu.
University of Maryland, Baltimore · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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).

Indexed as

Water MicrobiologyWater SupplyBangladeshCholeraClimateDisease OutbreaksHumansModels, StatisticalRisk FactorsRiversSeasonsTime Factors

Identifiers

PMID26526921
PMCPMC4674232
OpenAlexW2251117373

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.