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

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 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.

Figure 1

Daily incidence in the UK since May 12, 2020, compared with daily laboratory-confirmed cases and the ONS study (A) and daily prevalence in the UK compared with the ONS and REACT-1 studies (B) ONS data are taken from the report released on Oct 9, 2020, and ONS report dates are taken as the midpoint for the date range covered by the estimate. For the Symptom Study and REACT-1, the shaded areas represent 95% CIs. For the ONS, the shaded areas and error bars represent 95% credible intervals. ONS=Office for National Statistics. REACT-1=Real-time Assessment of Community Transmission.

Figure 2

Empirical probability density function of days to recovery with a gamma fit (A) and empirical cumulative density function of days to recovery with the same gamma fit (B) The bars represent empirical findings and the red line is the gamma fit.

Figure 3

Estimated R(t) for England between June 24 and Sept 28, 2020 The shaded area for Symptom Study data represents 95% credible intervals and for government data represents 95% CIs. UK Government estimates published every 7–12 days from June 12, 2020. R(t)=effective reproduction number.

Figure 4

Performance of our two ranking methods: ranking by prevalence and incidence on two metrics, recall at 20 (A) and the normalised mean reciprocal rank at 20 (B) Vertical lines represent 95% CIs.

Figure 5

Agreement between COVID Symptom Study and UK Government case numbers per week and UTLA, against the number of government pillar 2 tests carried out UTLA=Upper Tier Local Authority.