TY - JOUR
AU - Myall, Ashleigh C.
AU - Price, James R.
AU - Peach, Robert L.
AU - Abbas, Mohamed
AU - Mookerjee, Siddharth
AU - Zhu, Nina
AU - Ahmad, Isa
AU - Ming, Damien K.
AU - Ramzan, Farzan
AU - Teixeira, Daniel
AU - Graf, Christophe
AU - Weiße, Andrea Y.
AU - Harbarth, Stephan
AU - Holmes, Alison
AU - Barahona, Mauricio
PY - 2022
DA - August
TI - Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study
JO - The Lancet Digital Health
EP - e583
VL - 4
IS - 8
DO - https://doi.org/10.1016/S2589-7500(22)00093-0
AB - 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.
PB - Elsevier
UR - https://www.research.ed.ac.uk/en/publications/209012f8-bb0b-4a88-993d-6b16db715597
KW - Coronavirus (COVID-19)
KW - Healthcare associated infection
ER