Malaria affects three billion people worldwide. Despite remarkable reductions in malaria incidence over the last 15 years, recent evidence shows that our traditional control tools are weakening. Long-lasting insecticide-treated bednets (LLINs) are the most widely used tool for malaria prevention and have contributed significantly to decreases in malaria incidence, but recent studies suggest that LLINs are either less effective than before or people are not using them as reported. A rigorous assessment of how the timing and location of vector exposure intersects with real-life use of LLINs could be vitally important to regain the initiative in malaria control. However, the lack of a reliable measure of LLIN use presents a major challenge. Current measurement tools, like self-reported use, are subjective and unable to account for temporal variations in use. To address these limitations, I invented an electronic monitor of LLIN use. SmartNet uses sensors embedded in a standard LLIN to continuously assess whether it is unfurled with 98% accuracy. We have completed successful feasibility, acceptability and field trials of SmartNet. The central rationale for this project is that continuous monitoring of individual LLIN use combined with quantified exposure to malaria vectors will allow a more robust analysis than has previously been possible of how LLINs reduce vector exposure in practice. The research goal of this K23 proposal is to develop high-yield interventions for improving malaria control by identifying gaps between individual risk of vector exposure and individual LLIN use. To facilitate this work, I have access to a longitudinal cohort of 480 individuals in Uganda. Our approach leverages intensive entomology surveillance already being gathered every two weeks in this cohort. Additionally, we will deploy SmartNets over every sleeping space to cover every individual over multiple years. High-yield interventions will be identified by pursuing three specific aims: 1) quantify exposure to malaria vectors and identify factors associated with higher risk, 2) quantify LLIN use and identify factors associated with poor adherence and 3) identify mismatches between LLIN use and vector exposure, develop interventions addressing these gaps and then systematically determine the highest-yield interventions for reducing vector-human contact using a model of vector exposure. My long-term career goal is to establish an independent research career developing innovative approaches for improving malaria control. This K23 proposal supplements my prior experience with mentorship and training in malaria entomology and epidemiology and infectious disease modelling. Together, the proposed research activities and complementary training are designed to lead to a robust program of future work. I will emerge from this award prepared for a strong NIH R01 application to apply this approach in different transmission settings, to develop operational studies of the high-yield interventions we identify and to expand the scope from reducing vector-human contact to reducing actual malaria incidence. This K23 award provides the crucial link between my current experience and achieving my career goal of becoming an international leader inventing, deploying and testing innovative approaches for improving malaria prevention.
The proposed research is relevant to public health because our traditional tools for malaria control are weakening, including long-lasting insecticidal bednets that are the backbone of prevention and recommended for all 3 billion people affected by malaria world-wide. There is an urgent need for novel approaches to quantify individual risks of malaria exposure and design better interventions to address mismatches between the timing and location of vector exposure and the timing of bednet coverage. This proposed work is directly relevant to the NIAID initiative seeking to understand vector-host interactions in disease transmission by using an ICEMR cohort in Uganda to combine entomological surveillance and a new technology for measuring bednet use to identify high- yield interventions that will maximally reduce vector-human contact and improve malaria prevention.
