The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices

Abstract

Background: The COVID-19 epidemic has differentially impacted communities across England, with regional variation in rates of confirmed cases, hospitalisations and deaths. Measurement of this burden changed substantially over the first months, as surveillance was expanded to accommodate the escalating epidemic. Laboratory confirmation was initially restricted to clinical need ("pillar 1") before expanding to community-wide symptomatics ("pillar 2"). This study aimed to ascertain whether inconsistent measurement of case data resulting from varying testing coverage could be reconciled by drawing inference from COVID-19-related deaths.

Methods: We fit a Bayesian spatio-temporal model to weekly COVID-19-related deaths per local authority (LTLA) throughout the first wave (1 January 2020-30 June 2020), adjusting for the local epidemic timing and the age, deprivation and ethnic composition of its population. We combined predictions from this model with case data under community-wide, symptomatic testing and infection prevalence estimates from the ONS infection survey, to infer the likely trajectory of infections implied by the deaths in each LTLA.

Results: A model including temporally- and spatially-correlated random effects was found to best accommodate the observed variation in COVID-19-related deaths, after accounting for local population characteristics. Predicted case counts under community-wide symptomatic testing suggest a total of 275,000-420,000 cases over the first wave - a median of over 100,000 additional to the total confirmed in practice under varying testing coverage. This translates to a peak incidence of around 200,000 total infections per week across England. The extent to which estimated total infections are reflected in confirmed case counts was found to vary substantially across LTLAs, ranging from 7% in Leicester to 96% in Gloucester with a median of 23%.

Conclusions: Limitations in testing capacity biased the observed trajectory of COVID-19 infections throughout the first wave. Basing inference on COVID-19-related mortality and higher-coverage testing later in the time period, we could explore the extent of this bias more explicitly. Evidence points towards substantial under-representation of initial growth and peak magnitude of infections nationally, to which different parts of the country contribute unequally.

Fig. 1

Rates of COVID-19-related deaths and confirmed cases in England, by geography and week of death, and by lower-tier local authority (LTLA). (A, B): Weekly rates per 100,000 population of COVID-19-related deaths and confirmed cases, respectively, by geography type. Trajectories of reported deaths follow a smooth epidemic curve while the peak in case counts appears to be truncated across geographies outside of the early-affected London region, potentially as a result of national lockdown measures but also of testing constraints. Dashed vertical lines mark dates of significant policy changes with respect to confirmatory testing of suspect cases. (C, D): The same data instead presented as total rates per 100,000 per LTLA, across the entire first wave (1 January 2020 to 30 June 2020). Time periods are set according to the date of specimen and date of death, respectively

Fig. 2

Final model fit (1000 posterior samples) over time, as a national total and by geography type. The final model describes observed weekly COVID-19-related deaths per LTLA in terms of the size, age, ethnicity and deprivation level of the population, temporal trend and spatial correlation between neighbouring LTLAs. Observed rates of covid-related death per 100,000 population are shown in black (A) and white (B). Each grey/coloured line represents one sampled trajectory from the fitted model, and variation between these reflects uncertainty in the fit

Fig. 3

Predicted-P1+P2 cases, according to lagged and scaled-up predictions from the selected model for COVID-19-related deaths, in total (A and aggregated by geography type (B). 50–98% credible intervals are shown by the blue shaded areas. Observed totals of confirmed cases per week are indicated by black points - unfilled prior to P2-expansion and filled post-P2 expansion. Predicted-P1+P2 cases suggest the potential shape and magnitude of the first wave peak if community symptomatic testing (pillar 2) - in addition to hospital-based testing (pillar 1) - had been available from the beginning of the epidemic

Fig. 4

Comparison of predicted-P1+P2 cases assuming one-, two- and three-week lags between date of swabbing and date of death. Shaded intervals represent 50–98% credible intervals

Fig. 5

Estimated weekly incidence of infections in England (grey), inferred from predicted-P1+P2 cases (blue) and an assumed detection rate of 25% under expanded surveillance. Rate of detection is estimated by comparison of incidence estimates from the ONS infection survey (shown in red) and observed case counts (shown in black) between weeks starting 18 May to 15 June 2020. This rate is then applied to predicted-P1+P2 cases to obtain the estimated trajectory of total infections

Fig. 6

Estimated percentage of total infections represented in observed case counts, per LTLA (panels A and B) and per week (panel C), between 2020 and 01-01 and 2020-06-17. LTLAs of Gloucester and Teignbridge stand out as having the highest percentage of detected infections, with estimates of over 96%. Panel B illustrates the same estimates in panel A but grouped by region, against the total observed incidence per 100,000. Total infections over the time period are estimated based on the predicted-P1+P2 cases and an assumed infection detection rate of 25% under expanded surveillance. All panels reflect median predictions over 1000 posterior samples, with panel C additionally showing 50–98% credible intervals