Optimizing COVID-19 surveillance using historical electronic health records of influenza infections.

ABSTRACT: Targeting surveillance resources toward individuals at high risk of early infection can accelerate the detection of emerging outbreaks. However, it is unclear which individuals are at high risk without detailed data on interpersonal and physical contacts. We propose a data-driven COVID-19 surveillance strategy using Electronic Health Record (EHR) data that identifies the most vulnerable individuals who acquired the earliest infections during historical influenza seasons. Our simulations for all three networks demonstrate that the EHR-based strategy performs as well as the most-connected strategy. Compared to the random acquaintance surveillance, our EHR-based strategy detects the early warning signal and peak timing much earlier. On average, the EHR-based strategy has 9.8 days of early war

SUBMITTER: Du Z 

PROVIDER: S-EPMC9170911 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature