A novel hydrology-based malaria transmission model and field applications

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.

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.

NIH project details

Modeling
Tools for Elimination

Nov 2022 — Oct 2024

$221,250

United States

Larval source management