ArticlePLoS computational biology2021
Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model.
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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-driven strategies for model-informed decision-making during the COVID-19 pandemic: a systematic review.BMJ open · 2026Pooled it
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- An analysis of spatial and temporal uncertainty propagation in agent-based models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Decoding uncertainty for clinical decision-making.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Variance-based sensitivity analysis of tuberculosis transmission models.Journal of the Royal Society, Interface · 2022Article
- Exit strategies from lockdowns due to COVID-19: a scoping review.BMC public health · 2022Article
- Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.Epidemics · 2022Article
- Sensitivity analysis of queueing models based on polynomial chaos approach.Arabian journal of mathematics · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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