TY - JOUR
AU - Borgonovo, Emanuele
AU - Lu, Xuefei
AU - Rabitti, Giovanni
PY - 2022
DA - December
TI - Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions
JO - Journal of Computational and Graphical Statistics
DO - https://doi.org/10.1080/10618600.2022.2126483
AB - 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.
PB - Taylor and Francis
UR - https://researchportal.hw.ac.uk/en/publications/sensitivity-analysis-of-pandemic-models-can-support-effective-pol
KW - Coronavirus (COVID-19)
ER