Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions

Abstract

The COVID-19 pandemic has required international scientific efforts to address important aspects of the pandemic. Data science and scientific modeling are extensively used to provide assessments and predictions for policy-making purposes. However, resulting communications need to be supported by a proper uncertainty quantification to assess variability in model predictions, by the identification of the key-uncertainty drivers. This information can be provided by statisticians with sensitivity analysis methods. Knowing the drivers of uncertainty supports effective policy-making. Concerning the COVID-19 pandemic diffusion, two recent investigations reveal intervention-related parameters as more important than epidemiological parameters in two different modeling exercises. This result can help prioritize policy decisions.

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DOI

https://doi.org/10.1080/10618600.2022.2126483

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https://researchportal.hw.ac.uk/en/publications/sensitivity-analysis-of-pandemic-models-can-support-effective-pol