Reconstructing the Symptomatic-Case Epidemic from COVID-19-Related Deaths in England: A Spatiotemporal Modelling Study

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 biases in case data resulting from varying testing coverage could be addressed 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 - 30 June 2020), with respect to the local epidemic timing and the age, deprivation and ethnic composition of its population. We drew predictions averaging these sources of case-fatality variation, and back-translated according to a population case fatality ratio estimated under community-wide testing. Results: A model including temporally- and spatially-correlated random effects best accommodated the observed variation in mortality, after accounting for local population characteristics. Final predictions suggest a total of 276,219-420,491 cases over the first wave - an increase of 19-81% from the reported. Conclusions: Limitations in testing capacity biased the observed trajectory of COVID-19 cases 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 symptomatic infections nationally, to which different parts of the country contribute unequally.Funding Information: The following funding sources are acknowledged as providing funding for the named authors:This research was partly funded by the Bill & Melinda Gates Foundation (NTD Modelling Consortium OPP1184344: GFM; OPP1183986: ESN). Royal Society (Dorothy Hodgkin Fellowship: RL). Wellcome Trust (206250/Z/17/Z: TWR; 206471/Z/17/Z: OJB; 210758/Z/18/Z: SA). The following funding sources are acknowledged as providing funding for the working groupauthors:This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: JYL, KP, MJ, YL; INV-016832: SRP; NTD Modelling Consortium OPP1184344: CABP; OPP1139859: BJQ; OPP1191821: KO'R). BMGF (INV-016832; OPP1157270: KA). CADDE MR/S0195/1 & FAPESP 18/14389-0 (PM). EDCTP2 (RIA2020EF-2983-CSIGN: HPG). Elrha R2HC/UK FCDO/Wellcome Trust/This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care (KvZ). ERC Starting Grant (#757699: MQ). ERC (SG 757688: CJVA, KEA). This project has received funding from the European Union's Horizon 2020 research and innovation programme - project EpiPose (101003688: AG, KP, MJ, RCB, WJE, YL). FCDO/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP, KvZ). This research was partly funded by the Global Challenges Research Fund (GCRF) project 'RECAP' managed through RCUK and ESRC (ES/P010873/1: CIJ, TJ). HDR UK (MR/S003975/1: RME). HPRU (NIHR200908: NIB). Innovation Fund (01VSF18015: FK). MRC (MR/N013638/1: EF, NRW; MR/V027956/1: WW). Nakajima Foundation (AE). NIHR (16/136/46: BJQ; 16/137/109: BJQ, FYS, MJ, YL; 1R01AI141534-01A1: DH; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: TJ; NIHR200908: AJK, RME; NIHR200929: CVM, FGS, MJ, NGD; PR-OD-1017-20002: AR, WJE). Singapore Ministry of Health (RP). UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: HPG). UK MRC (MC_PC_19065 - Covid 19: Understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: NGD, RME, SC, TJ, WJE, YL; MR/P014658/1: GMK). Authors of this research receive funding from UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care (TJ). UKRI Research England (NGD). UKRI (MR/V028456/1: YJ). Wellcome Trust (206250/Z/17/Z: AJK; 208812/Z/17/Z: SC, SFlasche; 210758/Z/18/Z: JDM, JH, SFunk, SRM; 221303/Z/20/Z: MK; UNS110424: FK). No funding (AMF, DCT, YWDC).Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: Approval for the use of anonymised linelist data was granted by Public Health England and the Department for Health and Social Care. Consent of individuals was not required as no patient identifiable information was used.