Machine learning predicts hospital-onset COVID-19 infections using patient contact networks

Accurate and real-time disease prediction is vital for the prevention and control of healthcare-related infections. Although contacts between individuals are primarily responsible for infection chains, most prediction frameworks do not capture the contact dynamics. Researchers from the UK recently developed a real-time machine learning framework that uses dynamic patient contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the patient level. They then tested and validated the framework on international multi-site datasets across various epidemic and endemic periods. This study can be found on the medRxiv* preprint server.