Evidence map›Paper›PMID 32430840›Full record

ArticleEuropean journal of epidemiology2020

Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries.

Rajiv Chowdhury, Kevin Heng, Md Shajedur Rahman Shawon, Gabriel Goh, Daisy Okonofua, Carolina Ochoa-Rosales, Valentina Gonzalez-Jaramillo, Abbas Bhuiya, Daniel Reidpath, Shamini Prathapan and 5 more

Abstract read
In one paragraph

Article in European journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
122citing papers in PubMed, 4 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

122 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Managing cancer patients during the COVID-19 pandemic: an ESMO multidisciplinary expert consensus.Annals of oncology : official journal of the European Society for Medical Oncology · 2020
    Guideline
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  7. Review
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  18. Role of vaccine in fighting the variants of COVID-19.Chaos, solitons, and fractals · 2023
    Article
  19. Article
  20. Article

62 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

15 authors.

Rajiv ChowdhuryDepartment of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK. rc436@medschl.cam.ac.uk.ORCID http://orcid.org/0000-0003-4881-5690
Kevin HengCenter for Space and Habitability, University of Bern, Bern, Switzerland.
Md Shajedur Rahman ShawonCentre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Gabriel GohOpenAI Artificial Intelligence Research Laboratory, San Francisco, CA, USA.
Daisy OkonofuaDepartment of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Carolina Ochoa-RosalesDepartment of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands.
Valentina Gonzalez-JaramilloInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Abbas BhuiyaIndependent health and population researcher, Dhaka, Bangladesh.
Daniel ReidpathInternational Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Shamini PrathapanDepartment of Community Medicine, University of Sri Jayewardenepura, Colombo, Sri Lanka.
Sara ShahzadDepartment of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Christian L AlthausInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Nathalia Gonzalez-JaramilloInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Oscar H FrancoInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. oscar.franco@ispm.unibern.ch.
Global Dynamic Interventions Strategies for COVID-19 Collaborative Group

Funding

Medical Research Council MR/P02811X/1
6 · The paper itself

Abstract

To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs-increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal "break" when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R

Indexed as

CoronavirusBetacoronavirusCommunicable Disease ControlCoronavirus InfectionsCOVID-19HumansModels, TheoreticalPandemicsPneumonia, ViralSARS-CoV-2COVID-19Dynamic interventionsEpidemiologyInfectious diseasePrediction modelling

Identifiers

PMID32430840
PMCPMC7237242

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Registered trials

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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.