Evidence map›Paper›PMID 32461062›Full record

ArticleThe Journal of infection2020

Modelling SARS-COV2 Spread in London: Approaches to Lift the Lockdown.

Lara Goscé, Professor Andrew Phillips, Paula Spinola, Dr Rishi K Gupta, Professor Ibrahim Abubakar

Abstract read
In one paragraph

Article in The Journal of infection, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 5 pooled it
–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

40 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  5. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Lara GoscéUCL Institute for Global Health, London, UK. Electronic address: l.gosce@ucl.ac.uk.
Professor Andrew PhillipsUCL Institute for Global Health, London, UK.
Paula SpinolaUCL Institute for Global Health, London, UK.
Dr Rishi K GuptaUCL Institute for Global Health, London, UK.
Professor Ibrahim AbubakarUCL Institute for Global Health, London, UK.

Funding

Department of Health DRF-2018-11-ST2-004
6 · The paper itself

Abstract

objectiveTo use mathematical models to predict the epidemiological impact of lifting the lockdown in London, UK, and alternative strategies to help inform policy in the UK.

methodsA mathematical model for the transmission of SARS-CoV2 in London. The model was parametrised using data on notified cases, deaths, contacts, and mobility to analyse the epidemic in the UK capital. We investigated the impact of multiple non pharmaceutical interventions (NPIs) and combinations of these measures on future incidence of COVID-19.

resultsImmediate action at the early stages of an epidemic in the affected districts would have tackled spread. While an extended lockdown is highly effective, other measures such as shielding older populations, universal testing and facemasks can all potentially contribute to a reduction of infections and deaths. However, based on current evidence it seems unlikely they will be as effective as continued lockdown. In order to achieve elimination and lift lockdown within 5 months, the best strategy seems to be a combination of weekly universal testing, contact tracing and use of facemasks, with concurrent lockdown. This approach could potentially reduce deaths by 48% compared with continued lockdown alone.

conclusionsA combination of NPIs such as universal testing, contact tracing and mask use while under lockdown would be associated with least deaths and infections. This approach would require high uptake and sustained local effort but it is potentially feasible as may lead to elimination in a relatively short time scale.

Indexed as

BetacoronavirusInfection ControlAdolescentAdultAge FactorsChildChild, PreschoolCoronavirus InfectionsCOVID-19Health PolicyHumansInfantInfant, NewbornLondonMiddle AgedModels, Statistical

Identifiers

PMID32461062
PMCPMC7246004

What OpenQuestion holds

Textmetadata
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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.