Evidence map›Paper›PMID 33287789›Full record

ArticleBMC public health2020

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic.

David J Warne, Anthony Ebert, Christopher Drovandi, Wenbiao Hu, Antonietta Mira, Kerrie Mengersen

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.5field-weighted citation impact, top 26% of its field
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

9 citing papers in PubMed, 16 citations in OpenAlex.

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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

6 authors at 4 institutions in 3 countries.

David J WarneSchool of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia. david.warne@qut.edu.au.
Anthony EbertInstitute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.
Christopher DrovandiSchool of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia.
Wenbiao HuSchool of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
Antonietta MiraInstitute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.
Kerrie MengersenSchool of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia.
ARC Centre of Excellence for Mathematical and Statistical Frontiers · AUUniversità della Svizzera italiana · CHAustralian Research Council · AUQueensland University of Technology · AU

Funding

Australian Research Council (AU) DP200102101Australian Research Council (AU) LF150100150Horizon 2020 101016233Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung 105218_163196Swiss Data Science Center C17-12
6 · The paper itself

Abstract

backgroundThe global impact of COVID-19 and the country-specific responses to the pandemic provide an unparalleled opportunity to learn about different patterns of the outbreak and interventions. We model the global pattern of reported COVID-19 cases during the primary response period, with the aim of learning from the past to prepare for the future.

methodsUsing Bayesian methods, we analyse the response to the COVID-19 outbreak for 158 countries for the period 22 January to 9 June 2020. This encompasses the period in which many countries imposed a variety of response measures and initial relaxation strategies. Instead of modelling specific intervention types and timings for each country explicitly, we adopt a stochastic epidemiological model including a feedback mechanism on virus transmission to capture complex nonlinear dynamics arising from continuous changes in community behaviour in response to rising case numbers. We analyse the overall effect of interventions and community responses across diverse regions. This approach mitigates explicit consideration of issues such as period of infectivity and public adherence to government restrictions.

resultsCountries with the largest cumulative case tallies are characterised by a delayed response, whereas countries that avoid substantial community transmission during the period of study responded quickly. Countries that recovered rapidly also have a higher case identification rate and small numbers of undocumented community transmission at the early stages of the outbreak. We also demonstrate that uncertainty in numbers of undocumented infections dramatically impacts the risk of multiple waves. Our approach is also effective at pre-empting potential flare-ups.

conclusionsWe demonstrate the utility of modelling to interpret community behaviour in the early epidemic stages. Two lessons learnt that are important for the future are: i) countries that imposed strict containment measures early in the epidemic fared better with respect to numbers of reported cases; and ii) broader testing is required early in the epidemic to understand the magnitude of undocumented infections and recover rapidly. We conclude that clear patterns of containment are essential prior to relaxation of restrictions and show that modelling can provide insights to this end.

Indexed as

Global HealthBayes TheoremCOVID-19HumansPandemicsApproximate Bayesian computationCOVID-19SARS-CoV-2Sequential Monte CarloStochastic epidemiological models

Identifiers

PMID33287789
PMCPMC7719727
OpenAlexW3112212565

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.