@article{Borgonovo_E-2022_107742,
title = {Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions},
author = {Borgonovo, E. and Lu, X. and Rabitti, G.},
month = {dec},
year = {2022},
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.},
journal = {Journal of Computational and Graphical Statistics},
publisher = {Taylor and Francis},
url = {https://doi.org/10.1080/10618600.2022.2126483},
}
