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. 2008 Nov 18;105(46):17676-81.
doi: 10.1073/pnas.0809654105. Epub 2008 Nov 10.

Environmental signatures associated with cholera epidemics

Guillaume Constantin de Magny  1 , Raghu Murtugudde, Mathew R P Sapiano, Azhar Nizam, Christopher W Brown, Antonio J Busalacchi, Mohammad Yunus, G Balakrish Nair, Ana I Gil, Claudio F Lanata, John Calkins, Byomkesh Manna, Krishnan Rajendran, Mihir Kumar Bhattacharya, Anwar Huq, R Bradley Sack, Rita R Colwell
Affiliations

Environmental signatures associated with cholera epidemics

Guillaume Constantin de Magny et al. Proc Natl Acad Sci U S A. .

Abstract

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.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Map showing the region from which the environmental and epidemiological data were obtained. Locations for extraction of the monthly mean values for CHL and SST are indicated by (i) one degree resolution squares, orange for Kolkata (centered in 20.5N/88.5E) and white for Matlab (centered in 20.5N/90.5E), and for (ii) the five degree box average for both sites by the black solid line square (centered in 18.5N/89.5E). Locations where monthly mean rainfall values were obtained are indicated as follows: (i) two and half degree resolution points for Kolkata are delimited by a red dashed line square (centered in 23.75N/88.75E) and for Matlab by a red solid line square (centered in 23.75N/91.25E), and (ii) for the five degree box average for Kolkata by the yellow dashed line square (centered in 22.5N/87.5E) and for Matlab by the black dashed square (centered in 22.5N/90.0E). Background image represents the ground elevation coded from dark green, the lowest value, to white, the highest [The Global Land One-kilometer Base Elevation (GLOBE) NOAA, NGDC; (http://www.ngdc.noaa.gov/mgg/topo/globe.html); (48)].
Fig. 2.
Fig. 2.
Epidemiological and environmental dynamics for (A) Kolkata, India, and (B) Matlab, Bangladesh. The cholera cases, the CHL, the SST and the rainfall are shown in black, green, red and blue lines, respectively.
Fig. 3.
Fig. 3.
Observed epidemiological data and prediction of fitted models. For Kolkata, (A) temporal dynamics of observed cholera cases, fitted model and cross-validation model shown in black, red and blue, respectively. (B) Scatterplot of observed cholera cases against (i) predicted cholera cases by fitted model in red circles, and (ii) predicted cholera cases by cross-validation model in blue circles. Black line represents perfect agreement between predicted and observed cases. For Matlab, C same as A. D same as B.
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