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Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes
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Global sensitivity analysis in epidemiological modeling.

Xuefei Lu1, Emanuele Borgonovo2,3

  • 1SKEMA Business School, UniversitĂ© CĂ´te d'Azur, 5 Quai Marcel Dassault, Paris 92150, France.

European Journal of Operational Research
|November 22, 2021
PubMed
Summary
This summary is machine-generated.

Operations researchers use simulations for COVID-19 pandemic modeling. New methods reveal quarantine rate and intervention time significantly impact total infections and interactions.

Keywords:
AnalyticsCOVID-19 pandemicGlobal sensitivity analysisOR in pandemicsSIR models

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Area of Science:

  • Operations Research
  • Epidemiological Modeling
  • Data Science

Background:

  • Quantitative simulations are crucial for modeling the COVID-19 pandemic.
  • Uncertainty quantification and sensitivity analysis are essential for informed decision-making.

Purpose of the Study:

  • To develop a methodology for identifying key uncertainty drivers and interactions in epidemiological models.
  • To apply this methodology to a Susceptible-Infectious-Recovered (SIR) model for early COVID-19 pandemic data.

Main Methods:

  • Combined probabilistic sensitivity techniques with machine learning tools.
  • Applied the methodology to SIR models using early COVID-19 data from Italy and the U.S.
  • Analyzed both correlated and uncorrelated input scenarios.

Main Results:

  • Identified quarantine rate and intervention time as primary uncertainty drivers.
  • Demonstrated that these factors have opposing effects on the total number of infected individuals.
  • Quantified significant interactions between quarantine rate and intervention time.

Conclusions:

  • The developed methodology effectively identifies critical factors influencing pandemic dynamics.
  • Quarantine rate and intervention timing are key parameters for controlling COVID-19 spread.
  • Understanding parameter interactions is vital for accurate pandemic forecasting and policy.