Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.

ABSTRACT:

Background

Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.

Methods

We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 1

SUBMITTER: Myall A 

PROVIDER: S-EPMC9296105 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature