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.

Sensitivity analysis of pandemic models can support effective policy decisions

Borgonovo, Emanuele
Membro del Collaboration Group
;
Lu, Xuefei;Rabitti, Giovanni
Membro del Collaboration Group
In corso di stampa

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.
2022
Borgonovo, Emanuele; Lu, Xuefei; Rabitti, Giovanni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4052927
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