Precision recruitment for high-risk participants in a COVID-19 cohort study.

ABSTRACT:

Background

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 developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals.

Methods

We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk

SUBMITTER: Mezlini AM 

PROVIDER: S-EPMC10008035 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature