Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections

on behalf of the CMMID COVID-19 working group

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Abstract

Background: Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. Methods: Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever ≥ 37.5 °C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. Results: Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6 July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally, we estimated seroprevalence for each country. As of 7 June, our seroprevalence estimates range from 0% (many countries) to 13% (95% CrI 5.6–24%) (Belgium). Conclusions: We found substantial under-ascertainment of symptomatic cases, particularly at the peak of the first wave of the SARS-CoV-2 pandemic, in many countries. Reported case counts will therefore likely underestimate the rate of outbreak growth initially and underestimate the decline in the later stages of an epidemic. Although there was considerable under-reporting in many locations, our estimates were consistent with emerging serological data, suggesting that the proportion of each country’s population infected with SARS-CoV-2 worldwide is generally low.

Original languageEnglish
Article number332
JournalBMC Medicine
Volume18
Issue number1
DOIs
Publication statusPublished - 1 Dec 2020

Bibliographical note

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 (INV-003174: YL). DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: KvZ). Elrha R2HC/UK DFID/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). This project has received funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (101003688: WJE, YL). This research was partly funded by the Global Challenges Research Fund (GCRF) project ‘RECAP’ managed through RCUK and ESRC (ES/P010873/1: CIJ). HDR UK (MR/S003975/1: RME). NIHR (16/137/109: YL). UK DHSC/UK Aid/NIHR (ITCRZ 03010: HPG). UK MRC (MC_PC_19065: RME, WJE, YL). Wellcome Trust (206250/Z/17/Z: AJK, TWR; 210758/Z/18/Z: JH, SA). NG was partially funded by an ARC DECRA fellowship (DE180100635).

Funding Information:
The following authors were part of the Centre for Mathematical Modelling of Infectious Disease 2019-nCoV working group. Each contributed in processing, cleaning and interpretation of data, interpreted findings, contributed to the manuscript, and approved the work for publication: Arminder K Deol, C Julian Villabona-Arenas, Thibaut Jombart, Carl A B Pearson, Kathleen O?Reilly, James D Munday, Sophie R Meakin, Rachel Lowe, Amy Gimma, Akira Endo, Emily S Nightingale, Graham Medley, Anna M Foss, Gwenan M Knight, Kiesha Prem, St?phane Hu?, Charlie Diamond, James W Rudge, Katherine E. Atkins, Megan Auzenbergs, Stefan Flasche, Rein M G J Houben, Billy J Quilty, Petra Klepac, Matthew Quaife, Sebastian Funk, Quentin J Leclerc, Jon C Emery, Mark Jit, David Simons, Nikos I Bosse, Simon R Procter, Fiona Yueqian Sun, Samuel Clifford, Katharine Sherratt, Alicia Rosello, Nicholas G. Davies, Oliver Brady, Damien C Tully and Georgia R Gore-Langton. The authors, on behalf of the Centre for the Mathematical Modelling of Infectious Diseases (CMMID) COVID-19 working group, wish to thank the Defence Science and Technology Laboratory (Dstl) for providing the High Performance Computing facilities and associated expertise that has enabled these models to be prepared, run and processed in an appropriately rapid and highly efficient manner. Dstl is part of the Ministry of Defence.

Funding Information:
The following funding sources are acknowledged as providing funding for the working group authors. Alan Turing Institute (AE). BBSRC LIDP (BB/M009513/1: DS). This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: KP, MJ; NTD Modelling Consortium OPP1184344: CABP, GM; OPP1180644: SRP; OPP1183986: ESN; OPP1191821: KO’R, MA). DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP). DTRA (HDTRA1-18-1-0051: JWR). ERC Starting Grant (#757688: CJVA, KEA; #757699: JCE, RMGJH; 757699: MQ). This project has received funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (101003688: KP, MJ, PK). This research was partly funded by the Global Challenges Research Fund (GCRF) project ‘RECAP’ managed through RCUK and ESRC (ES/P010873/1: AG, TJ). Nakajima Foundation (AE). 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 (16/136/46: BJQ; 16/137/109: BJQ, CD, FYS, MJ; Health Protection Research Unit for Immunisation NIHR200929: NGD; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: TJ; NIHR200929: MJ; PR-OD-1017-20002: AR). Royal Society (Dorothy Hodgkin Fellowship: RL; RP\EA\180004: PK). UK MRC (LID DTP MR/N013638/1: GRGL, QJL; MC_PC_19065: AG, NGD, SC, TJ; MR/P014658/1: GMK). Authors of this research receive funding from UK Public Health Rapid Support Team funded by the UK Department of Health and Social Care (TJ). Wellcome Trust (206471/Z/17/Z: OJB; 208812/Z/17/Z: SC, SFlasche; 210758/Z/18/Z: JDM, KS, NIB, SFunk, SRM). No funding (AKD, AMF, DCT, SH).

Funding Information:
Defence Science and Technology Laboratory High Performance Computing support has been funded by the Ministry of Defence Chief Scientific Advisor. Acknowledgements

Publisher Copyright:
© 2020, The Author(s).

Keywords

  • Case ascertainment
  • COVID-19
  • Outbreak analysis
  • SARS-CoV-2
  • Situational awareness
  • Surveillance
  • Under-reporting

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