ABSTRACT: Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We describe an approach for reducing recruiting time and resources in a COVID-19 study by targeting recruitment to high-risk individuals. Our approach is based on direct and longitudinal connection with research participants and computes individual risk scores from individually permissioned data about socioeconomic and behavioural data, in combination with predicted local prevalence data. When we used these scores to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence compared with similar real-world study cohorts.
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