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Global sensitivity analysis in epidemiological modeling.
Lu, Xuefei; Borgonovo, Emanuele.
  • Lu X; SKEMA Business School, Université Côte d'Azur, 5 Quai Marcel Dassault, Paris 92150, France.
  • Borgonovo E; Department of Decision Sciences, Bocconi University, Via Röntgen 1, Milan 20136, Italy.
Eur J Oper Res ; 304(1): 9-24, 2023 Jan 01.
Article in English | MEDLINE | ID: covidwho-2309663
ABSTRACT
Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Language: English Journal: Eur J Oper Res Year: 2023 Document Type: Article Affiliation country: J.ejor.2021.11.018

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Language: English Journal: Eur J Oper Res Year: 2023 Document Type: Article Affiliation country: J.ejor.2021.11.018