Predictive modeling of cholera using GRACE and TRMM satellite data

Monday, 14 December 2015
Poster Hall (Moscone South)
Antarpreet Jutla, West Virginia University, Morgantown, WV, United States, Ali S Akanda, University of Rhode Island, Kingston, RI, United States and Rita R Colwell, University of Maryland College Park, Centre for Bioinformatics and Computational Biology, College Park, MD, United States
Cholera outbreaks can be classified in three forms– epidemic (sudden or seasonal outbreaks), endemic (recurrence and persistence of the disease for several consecutive years) and mixed-mode endemic (combination of certain epidemic and endemic conditions) with significant spatial and temporal heterogeneity. Endemic cholera is related to floods and droughts in regions where water and sanitation infrastructure are inadequate or insufficient. With more than a decade of terrestrial water storage (TWS) data obtained from Gravity Recovery and Climate Experiment (GRACE), understanding dynamics of river discharge is now feasible. We explored lead-lag relationships between TWS in the Ganges-Brahmaputra-Meghna (GBM) basin and endemic cholera in Bangladesh. Since bimodal seasonal peaks in cholera in Bangladesh occur during the spring and autumn season, two separate models, between TWS and disease time series (2002 to 2010) were developed. TWS, hence water availability, showed an asymmetrical, strong association with spring (τ=-0.53; p<0.001) and autumn (τ=0.45; p<0.001) cholera prevalence up to five to six months in advance. One unit (cm 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 season, while an increase in regional water by one unit, through floods, increased odds of above average cholera in the autumn by 29% [CI:22-33%; p<0.05]. Epidemic cholera is related with warm temperatures and heavy rainfall. Using TRMM data for several locations in Asia and Africa, probability of cholera increases 18% [CI:15-23%; p<0.05] after heavy precipitation resulted in a societal conditions where access to safe water and sanitation was disrupted. Results from mechanistic modeling framework using systems approach that include satellite based hydroclimatic information with tradition disease transmission models will also be presented.