Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study.
Lancet Public Health
; 6(1): e21-e29, 2021 01.
Article
in English
| MEDLINE | ID: covidwho-1072036
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 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 2â873â726 users living in England signed up to use the app, of whom 2â842â732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120â192â306 daily reports of their symptoms, and recorded the results of 169â682 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 15â841 (95% CI 14â023-17â885) 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Self Report
/
Public Health Surveillance
/
Mobile Applications
/
Disease Hotspot
/
COVID-19
Type of study:
Cohort study
/
Diagnostic study
/
Observational study
/
Prognostic study
Topics:
Variants
Limits:
Adolescent
/
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
/
Young adult
Country/Region as subject:
Europa
Language:
English
Journal:
Lancet Public Health
Year:
2021
Document Type:
Article
Affiliation country:
S2468-2667(20)30269-3
Similar
MEDLINE
...
LILACS
LIS