Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions

Emanuele Borgonovo, Xuefei Lu, Giovanni Rabitti

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
2 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)767-768
Number of pages2
JournalJournal of Computational and Graphical Statistics
Issue number3
Early online date19 Sept 2022
Publication statusPublished - 3 Jul 2023


  • COVID-19 pandemic
  • Global sensitivity analysis
  • SIR models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics


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