Evidence map›Paper›PMID 34534205›Full record

ArticlePLoS computational biology2021

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model.

Federica Gugole, Luc E Coffeng, Wouter Edeling, Benjamin Sanderse, Sake J de Vlas, Daan Crommelin

Abstract read
In one paragraph

Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. An analysis of spatial and temporal uncertainty propagation in agent-based models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
    Article
  4. Decoding uncertainty for clinical decision-making.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Article
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.

Federica GugoleCentrum Wiskunde & Informatica, Amsterdam, The Netherlands.ORCID 0000-0003-1988-3555
Luc E CoffengDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0002-4425-2264
Wouter EdelingCentrum Wiskunde & Informatica, Amsterdam, The Netherlands.
Benjamin SanderseCentrum Wiskunde & Informatica, Amsterdam, The Netherlands.ORCID 0000-0001-9483-1988
Sake J de VlasDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0002-1830-5668
Daan CrommelinCentrum Wiskunde & Informatica, Amsterdam, The Netherlands.ORCID 0000-0003-0542-6337

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.

Indexed as

Computer SimulationUncertaintyCommunicable Disease ControlContact TracingCOVID-19HumansProbabilitySARS-CoV-2

Identifiers

PMID34534205
PMCPMC8480746

What OpenQuestion holds

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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.