Environmental signatures associated with cholera epidemics

TitleEnvironmental signatures associated with cholera epidemics
Publication TypeJournal Articles
Year of Publication2008
AuthorsG. de Magny C, Murtugudde R., Sapiano M.RP, Nizam A., Brown C.W, Busalacchi A.J, Yunus M., Nair G.B, Gil A.I, Lanata C.F, Colwell RR
JournalProceedings of the National Academy of SciencesProceedings of the National Academy of Sciences
Type of Article10.1073/pnas.0809654105
ISBN Number0027-8424, 1091-6490

The causative agent of cholera, Vibrio cholerae, has been shown to be autochthonous to riverine, estuarine, and coastal waters along with its host, the copepod, a significant member of the zooplankton community. Temperature, salinity, rainfall and plankton have proven to be important factors in the ecology of V. cholerae, influencing the transmission of the disease in those regions of the world where the human population relies on untreated water as a source of drinking water. In this study, the pattern of cholera outbreaks during 1998–2006 in Kolkata, India, and Matlab, Bangladesh, and the earth observation data were analyzed with the objective of developing a prediction model for cholera. Satellite sensors were used to measure chlorophyll a concentration (CHL) and sea surface temperature (SST). In addition, rainfall data were obtained from both satellite and in situ gauge measurements. From the analyses, a statistically significant relationship between the time series for cholera in Kolkata, India, and CHL and rainfall anomalies was determined. A statistically significant one month lag was observed between CHL anomaly and number of cholera cases in Matlab, Bangladesh. From the results of the study, it is concluded that ocean and climate patterns are useful predictors of cholera epidemics, with the dynamics of endemic cholera being related to climate and/or changes in the aquatic ecosystem. When the ecology of V. cholerae is considered in predictive models, a robust early warning system for cholera in endemic regions of the world can be developed for public health planning and decision making.ecology epidemiology microbiology remote sensing