Hydrology, remote sensing and water related diseases: Predicting cholera outbreaks in Bengal delta.

摘要

There is growing evidence that outbreaks of several water-related diseases are potentially predictable by using satellite derived macro-scale environmental variables. This research addresses cholera, which one of the most prevalent water-related infections in the tropical regions of the world. Since the macro-scale environment provides natural ecological niche for Vibrio cholerae, causative agent for disease outbreaks, and a powerful evidence of new biotypes is emerging, it is highly unlikely that cholera will ever be fully eradicated. Consequently, to develop effective intervention and mitigation strategies to reduce disease burden, it is necessary to develop cholera prediction mechanisms with several months' lead-time. Satellite data provides reliable estimates of plankton abundance, through chlorophyll, as well as reflectances which can form the basis of early warning models. Within this context, the overall goal of the proposed research is to develop a seasonal cholera prediction model with two to three months lead time, using primarily remote sensing data. Three closely related objectives of this research are to: (i) determine the space-time structure of chlorophyll in the Bay of Bengal, (ii) evaluate role of freshwater discharge in creating seasonality and relationships among phytoplankton and sea surface temperature, (iii) develop a cholera prediction modeling framework. This research shows, that seasonal cholera outbreaks in the Bengal Delta can be predicted two to three months in advance with an overall prediction accuracy of over 75% by using combinations of satellite-derived chlorophyll and air temperature. Such high prediction accuracy is achievable because the two seasonal peaks of cholera are predicted using two separate models representing distinctive macro-scale environmental processes. We have shown that interannual variability of pre-monsoon cholera outbreaks can be satisfactorily explained with coastal plankton blooms and a cascade of hydro-coastal processes. Thereafter, a new remote sensing reflectance based statistical index: Satellite Water Impurity Marker, or SWIM is developed to estimate impurity levels in the coastal waters and is based on the variability observed between blue and green reflectance (i.e., clear and impure water). The index can predict cholera outbreaks in the Bengal Delta with 78% accuracy with two months lead time. Our results clearly demonstrate that satellite data over a range of space and time scales can be very effective in developing a cholera prediction model for the disease endemic regions.