Evidence map›Paper›PMID 33087179›Full record

ArticleBMC medicine2020

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

Timothy W Russell, Nick Golding, Joel Hellewell, Sam Abbott, Lawrence Wright, Carl A B Pearson, Kevin van Zandvoort, Christopher I Jarvis, Hamish Gibbs, Yang Liu and 4 more

Abstract read
In one paragraph

Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.

0numbers the graph read from it
0cells of the map it votes in
120citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

120 citing papers in PubMed.

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  17. Transcultural psychiatry · 2024
    Article
  18. Article
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60 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Timothy W RussellCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK. timothy.russell@lshtm.ac.uk.ORCID http://orcid.org/0000-0001-5610-6080
Nick GoldingTelethon Kids Institute and Curtin University, Perth, Western Australia, Australia.ORCID http://orcid.org/0000-0001-8916-5570
Joel HellewellCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0003-2683-0849
Sam AbbottCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0001-8057-8037
Lawrence WrightDefence Science and Technology Laboratory/Sopra Steria, Fareham, UK.
Carl A B PearsonCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0003-0701-7860
Kevin van ZandvoortCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-7770-0537
Christopher I JarvisCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-0812-2446
Hamish GibbsCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0003-4413-453X
Yang LiuCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-9368-0425
Rosalind M EggoCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-0362-6717
W John EdmundsCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-9179-2917
Adam J KucharskiCentre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0001-8814-9421
CMMID COVID-19 working group

Funding

Medical Research Council MC_PC_19065Medical Research Council MR/S003975/1Wellcome Trust 206250/Z/17/ZWellcome Trust 206471/Z/17/ZWellcome Trust 210758/Z/18/ZWellcome Trust 221303/Z/20/Z
6 · The paper itself

Abstract

backgroundAsymptomatic 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.

methodsUsing 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.

resultsBased 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).

conclusionsWe 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.

Indexed as

PandemicsBayes TheoremBetacoronavirusCoronavirus InfectionsCOVID-19HumansPneumonia, ViralSARS-CoV-2Seroepidemiologic StudiesCase ascertainmentCOVID-19Outbreak analysisSARS-CoV-2Situational awarenessSurveillanceUnder-reporting

Identifiers

PMID33087179
PMCPMC7577796

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.