A Novel Hydrology-based Malaria Transmission Model and Field Applications
Project Summary
Malaria is a major public health challenge in Africa. Scale-up of insecticide-treated nets (LLINs) and
indoor residual spray (IRS) in the past two decades has reduced malaria burden in Africa by half,
however the progress of malaria control has been stalled in many African countries due to limited
effectiveness of LLINs and IRS. The World Health Organization recommends larval source
management (LSM) as a supplementary vector control tool. However, LSM has so far not been widely
used for malaria vector control in Africa, partly due to the inability to predict habitat locations and stability
in many eco-epidemiological settings. LSM would be greatly facilitated if larval habitat distribution can
be predicted a priori so that areas best suitable or unsuitable to LSM can be identified. Further, if the
impact of environmental modifications such as landscape alteration and irrigation on malaria risk can
be predicted, optimal LSM-based vector control program can be developed. Past studies have
attempted to use field-based surveys or remotely sensed data for larval habitat identification or
correlated environmental factors with malaria risk, but these studies focused on the statistical
association between the environmental factors and malaria incidence, and they did not consider the
physical processes and environmental regulation on vector larval ecology. Furthermore, the dynamic
nature of the interactions between the multiple environmental factors that may be highly dynamic and
malaria risk was not studied. Recent advancements in parallel computing, hydrological modeling and
remote sensing present an excellent opportunity to incorporate hydrologic processes in malaria risk
modeling, and subsequently enhance the prediction accuracy. The central objective of this R21
application is to integrate a physically-based hydrologic model with remote sensing and
entomological data, to model malaria risk and apply the model to identify optimal larval habitat
water management strategies and malaria hotspots. Well-characterized study sites in western
Kenya with detailed entomological and epidemiological information will be used to calibrate and validate
the model. A unique aspect of this project is the use of multi-layer data such as hydrological,
meteorological, topographic, entomological and historical epidemiological parameters to enhance
malaria risk prediction. The findings of this project will improve our understanding of the impact
of hydrology and other environmental conditions on vector ecology and malaria risk, and
enhance malaria control through a priori prediction of transmission hotspots at high spatial
resolution and identification of optimal agricultural water management strategies that meet the
crop production needs but reduce malaria transmission.