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Drivers of epidemic dynamics in real time from daily digital COVID-19 measurements.
Kendall, Michelle; Ferretti, Luca; Wymant, Chris; Tsallis, Daphne; Petrie, James; Di Francia, Andrea; Di Lauro, Francesco; Abeler-Dörner, Lucie; Manley, Harrison; Panovska-Griffiths, Jasmina; Ledda, Alice; Didelot, Xavier; Fraser, Christophe.
Afiliación
  • Kendall M; Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.
  • Ferretti L; Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, UK.
  • Wymant C; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford OX3 7LF, UK.
  • Tsallis D; Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, UK.
  • Petrie J; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford OX3 7LF, UK.
  • Di Francia A; Zühlke Engineering Ltd., 80 Great Eastern Street, London EC2A 3JL, UK.
  • Di Lauro F; Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, UK.
  • Abeler-Dörner L; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford OX3 7LF, UK.
  • Manley H; UK Health Security Agency, Nobel House, 17 Smith Square, London SW1P 3JR, UK.
  • Panovska-Griffiths J; Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, UK.
  • Ledda A; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford OX3 7LF, UK.
  • Didelot X; Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, UK.
  • Fraser C; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford OX3 7LF, UK.
Science ; 385(6710): eadm8103, 2024 Aug 16.
Article en En | MEDLINE | ID: mdl-38991048
ABSTRACT
Understanding the drivers of respiratory pathogen spread is challenging, particularly in a timely manner during an ongoing epidemic. In this work, we present insights that we obtained using daily data from the National Health Service COVID-19 app for England and Wales and that we shared with health authorities in almost real time. Our indicator of the reproduction number R(t) was available days earlier than other estimates, with an innovative capability to decompose R(t) into contact rates and probabilities of infection. When Omicron arrived, the main epidemic driver switched from contacts to transmissibility. We separated contacts and transmissions by day of exposure and setting and found pronounced variability over days of the week and during Christmas holidays and events. For example, during the Euro football tournament in 2021, days with England matches showed sharp spikes in exposures and transmissibility. Digital contact-tracing technologies can help control epidemics not only by directly preventing transmissions but also by enabling rapid analysis at scale and with unprecedented resolution.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Trazado de Contacto / SARS-CoV-2 / COVID-19 Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Science Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Trazado de Contacto / SARS-CoV-2 / COVID-19 Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Science Año: 2024 Tipo del documento: Article País de afiliación: Reino Unido