The National Institute of Allergy and Infectious Diseases (NIAID) recently awarded multi-investigators, Guiyun Yan, PhD, professor of population health & disease prevention at the UCI Program in Public Health, and Hsu Kuo-Lin, PhD, professor of civil and environmental engineering at the UCI Samueli School of Engineering, a two-year research grant to develop a novel hydrology-based model to facilitate malaria control in Africa.
For the past two decades, insecticide-treated nets and indoor residual spray have reduced malaria burden in Africa by half but progress is still needed to reduce this public health problem. The World Health Organization recommends larval source management (LSM) in suitable settings as a supplementary vector control tool that can move the needle in reducing malaria transmission.
Larvae are exclusively aquatic so the locations of their habitats are stagnant water bodies like wells, ricefields, pools, marshes, and human-made pits. LSM has so far not been widely used partly due to the inability to predict the locations with a fine spatial resolution. LSM would be greatly facilitated if larval habitat distribution can be predicted in advance so that areas best suitable or unsuitable to LSM can be identified, and targeted habitat treatment can be applied. Further, if the impact of environmental modifications such as landscape alteration and irrigation and climate changes on malaria risk can be predicted, optimal LSM-based vector control programs can be developed.
Unique collaboration to tackle malaria burden
This project aims to address those shortcomings to LSM strategies by integrating a physically-based, hydrologic model with remote sensing and entomological data, to model malaria risk. This model will be able to target malaria hotspots for optimal larval habitat water management strategies. A noteworthy 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 help develop optimal water management strategies that meet the needs of crop production but reduce malaria transmission.”
– Guiyan Yan, PhD
Kuo-Lin’s engineering expertise of hydrologic systems complements Yan’s extensive research on malaria prevention and control. 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.
“Our interdisciplinary collaboration for this project is a prime example of the rich research community here at UCI,” says Yan. “The findings of this project will help develop optimal water management strategies that meet the needs of crop production but reduce malaria transmission.”
