Training of machine learning algorithms for the classification of accelerometer-measured bednet use and related behaviors associated with malaria risk

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PROJECT SUMMARY/ABSTRACT 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 de- creases 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 the timing and circumstances of LLIN use could be vitally important to regain the initiative in malaria control, but we lack a reliable measure of LLIN use. Current measurement tools, like self-reported use, are subjective and unable to account for temporal variations in use. I previously invented an electronic monitor of LLIN use to address these limitations. In my NIAID-funded K23 work we use these tools to measure LLIN use related to malaria exposure. Most recently, I have pioneered a vastly improved approach using machine learning algorithms and accelerometer-based sensors to measure LLIN use. The central rationale for this project is that by leveraging this novel accelerometer LLIN use monitor we can be more ambitious with our goals for measuring malaria risk related to LLINs. When combined with a carefully trained machine learning algorithm, I believe that we can accurately classify a far wider range of behaviors than merely when an LLIN is used. In this study, we will additionally 1) measure if there is someone under the LLIN, 2) determine how many people are under the LLIN, 3) identify interruptions in LLIN use (such as entering or exiting an LLIN), and 4) characterize who is under the LLIN (e.g. adult versus child). Longitudinal surveillance of exactly this type of data is crucial to disentangle the role of LLINs in malaria prevention. The immediate goal of this R21 proposal is to train a comprehensive platform for highly accurate remote monitoring of LLIN use and other behaviors related to malaria risk. Our approach thus gathers data from the community, trains a machine learning algorithm and then tests the real-life accuracy of that system. We will pursue our research goal by with these three aims: 1) gather real-life data about how LLINs are hung and used in the community, 2) train the machine learning algorithms based off pre-defined protocols informed by actual practice and 3) test the accuracy of the machine learning algorithms in real-life settings. This high-risk, high-reward proposal represents an inno- vative approach to answer a pressing question that limits our understanding of how LLINs prevent malaria: when and how are LLINs used in practice? The long term goal for this novel surveillance platform is to develop a tool for measuring LLIN use and related behaviors to point the way towards identifying better interventions for malaria prevention. The single accelerometer and generality in the training of the machine were purposefully chosen to facilitate adoption of this approach for malaria surveillance in other settings. This proposal will establish a firm foundation upon which to pursue future research studies using this novel tool and prepare me for an independent research career using novel technologies and research methods to improve malaria control.