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: We performed modelling on longitudinal, self-reported data from users of the COVID Symptom Study app in England between 24 March and 29 September, 2020. Combining a symptom-based predictive model for COVID-19 positivity and RT-PCR tests provided by the Department of Health we were able to estimate disease incidence, prevalence and effective reproduction number. Geographically granular estimates were used to highlight regions with rapidly increasing case numbers, or hotspots.

Findings: More than 2.8 million app users in England provided 120 million daily reports of their symptoms, and recorded the results of 170,000 PCR tests. On a national level our estimates of incidence and prevalence showed similar sensitivity to changes as two national community surveys: the ONS and REACT-1 studies. On 28 September 2020 we estimated 15,841 (95% CI 14,023-17,885) daily cases, a prevalence of 0.53% (95% CI 0.45-0.60), and R(t) of 1.17 (95% credible interval 1.15-1.19) in England. On a geographically granular level, on 28 September 2020 we detected 15 of the 20 regions with highest incidence according to Government test data, with indications that our method may be able to detect rapid case increases in regions where Government testing provision is more limited.

Interpretation: Self-reported data from mobile applications can provide an agile resource to inform policymakers during a fast-moving pandemic, serving as an independent and complementary resource to more traditional instruments for disease surveillance.

Funding: Zoe Global Limited, Department of Health, Wellcome Trust, EPSRC, NIHR, MRC, Alzheimer's Society.

Figure 1.

A) Daily incidence since 12 May in the UK compared against daily lab-confirmed cases and the ONS study B) Daily prevalence in the UK, compared with the ONS and REACT-1 studies. ONS data is taken from the report released on 9 October 2020. ONS report dates are taken as the midpoint for the date range covered by the estimate.

Figure 2.

Left: Empirical PDF of days to recovery along with a Gamma fit. Right: Empirical CDF of days to recovery along with the same Gamma fit.

Figure 3.

Estimated R(t), for NHS regions in England between 24 June and 28 September, with 95% credible intervals shown. UK government estimates published every 7–12 days from 12 June.

Figure 4.

Performance of our two ranking methods: ranking by prevalence and incidence on two metrics, Recall @ 20 and the Normalised Mean Reciprocal Rank.

Figure 5.

Agreement between Symptom Study and Government case numbers per week and UTLA, against the number of Government Pillar 2 tests carried out.