HYDROLOGY, REMOTE SENSING AND WATER RELATED DISEASES: PREDICTING CHOLERA OUTBREAKS IN BENGAL DELTA.
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, ... read morecausative 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.
Thesis (Ph.D.)--Tufts University, 2011.
Submitted to the Dept. of Civil Engineering.
Advisor: Shafiqul Islam.
Committee: Richard Vogel, Jeffrey Griffiths, and Ignacio Rodriguez Iturbe.
Keywords: Hydrologic sciences, Civil engineering, and Water resources management.read less
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