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Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application : a prospective, observational study

Varsavsky, Thomas ; Graham, Mark S. ; Canas, Liane S. ; Ganesh, Sajaysurya ; Capdevila Pujol, Joan ; Sudre, Carole H. ; Murray, Benjamin ; Modat, Marc ; Jorge Cardoso, M. and Astley, Christina M. , et al. (2021) In The Lancet Public Health 6(1). p.21-29
Abstract

Background: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods: In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated... (More)

Background: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods: In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots. Findings: From March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023–17 885) daily cases, a prevalence of 0·53% (0·45–0·60), and R(t) of 1·17 (1·15–1·19) in England. On a geographically granular level, on Sept 28, 2020, we detected 15 (75%) of the 20 regions with highest incidence according to government test data. Interpretation: Our method could help to detect rapid case increases in regions where government testing provision is lower. Self-reported data from mobile applications can provide an agile resource to inform policy makers during a quickly moving pandemic, serving as a complementary resource to more traditional instruments for disease surveillance. Funding: Zoe Global, UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Alzheimer's Society, Chronic Disease Research Foundation.

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publishing date
type
Contribution to journal
publication status
published
subject
in
The Lancet Public Health
volume
6
issue
1
pages
21 - 29
publisher
Elsevier
external identifiers
  • pmid:33278917
  • scopus:85099010576
ISSN
2468-2667
DOI
10.1016/S2468-2667(20)30269-3
language
English
LU publication?
yes
id
746beb6f-244f-4820-a876-4626326b9848
date added to LUP
2021-01-20 09:28:54
date last changed
2024-04-18 00:43:40
@article{746beb6f-244f-4820-a876-4626326b9848,
  abstract     = {{<p>Background: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods: In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots. Findings: From March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023–17 885) daily cases, a prevalence of 0·53% (0·45–0·60), and R(t) of 1·17 (1·15–1·19) in England. On a geographically granular level, on Sept 28, 2020, we detected 15 (75%) of the 20 regions with highest incidence according to government test data. Interpretation: Our method could help to detect rapid case increases in regions where government testing provision is lower. Self-reported data from mobile applications can provide an agile resource to inform policy makers during a quickly moving pandemic, serving as a complementary resource to more traditional instruments for disease surveillance. Funding: Zoe Global, UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Alzheimer's Society, Chronic Disease Research Foundation.</p>}},
  author       = {{Varsavsky, Thomas and Graham, Mark S. and Canas, Liane S. and Ganesh, Sajaysurya and Capdevila Pujol, Joan and Sudre, Carole H. and Murray, Benjamin and Modat, Marc and Jorge Cardoso, M. and Astley, Christina M. and Drew, David A. and Nguyen, Long H. and Fall, Tove and Gomez, Maria F. and Franks, Paul W. and Chan, Andrew T. and Davies, Richard and Wolf, Jonathan and Steves, Claire J. and Spector, Tim D. and Ourselin, Sebastien}},
  issn         = {{2468-2667}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{21--29}},
  publisher    = {{Elsevier}},
  series       = {{The Lancet Public Health}},
  title        = {{Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application : a prospective, observational study}},
  url          = {{http://dx.doi.org/10.1016/S2468-2667(20)30269-3}},
  doi          = {{10.1016/S2468-2667(20)30269-3}},
  volume       = {{6}},
  year         = {{2021}},
}