Crossmark

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

Crossref DOI link: https://doi.org/10.1016/S2589-7500(22)00093-0

Published Print: 2022-08

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Myall, Ashleigh

Price, James R

Peach, Robert L

Abbas, Mohamed

Mookerjee, Sid

Zhu, Nina

Ahmad, Isa

Ming, Damien

Ramzan, Farzan

Teixeira, Daniel

Graf, Christophe

Weiße, Andrea Y

Harbarth, Stephan

Holmes, Alison

Barahona, Mauricio
Funding

Funding for this research was provided by:

Imperial College London (EP/N014529/1)

Imperial College London (MRF-145–0004-TPG-AVISO)

Imperial College London (NIHR200876)

World Health Organization

Deutsche Forschungsgemeinschaft (Project-ID 424778381-TRR 295)

UK Research and Innovation

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

National Institute for Health Research Health Protection Research Unit

NIHR Imperial Biomedical Research Centre

University of Warwick

Engineering and Physical Sciences Research Council
License Information

Text and Data Mining valid from 2022-08-01

Text and Data Mining valid from 2022-08-01

Version of Record valid from 2022-04-27
More Information

This article is maintained by: Elsevier

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

Journal Title: The Lancet Digital Health

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/S2589-7500(22)00093-0

Content Type: article

Copyright: © 2022 The Author(s). Published by Elsevier Ltd.