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Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization
Fran Sérgio Lobato1, Gustavo Barbosa Libotte2, Gustavo Mendes Platt3
1Chemical Engineering Faculty, Federal University of Uberlândia, Uberlândia, Brazil.
Computational and Mathematical Methods in Medicine
|October 21, 2020
Summary
This study introduces a robust inverse problem approach for the Susceptible, Infected, Dead, and Recovered (SIDR) model to improve COVID-19 parameter estimation. Considering design variable sensitivity yields more reliable epidemic dynamics results.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Computational Science
Background:
- Traditional inverse problem solutions often neglect uncertainties in models and parameters.
- Ignoring the influence of design variable sensitivity can lead to simplified, less accurate estimations.
- Accurate parameter identification is crucial for understanding and managing infectious disease dynamics.
Purpose of the Study:
- To develop a robust inverse problem formulation for parameter estimation in epidemic modeling.
- To incorporate the sensitivity of design variables into the estimation process for enhanced reliability.
- To simulate COVID-19 dynamics using the Susceptible, Infected, Dead, and Recovered (SIDR) model.
Main Methods:
- Formulation of a robust multiobjective optimization problem.
- Minimization of estimation uncertainties and maximization of a robustness parameter.
- Application of the Multiobjective Stochastic Fractal Search algorithm with the Effective Mean concept.
Main Results:
- Parameter estimation for the SIDR model was performed using real COVID-19 data from China.
- The robust inverse problem approach demonstrated improved reliability in results.
- Evaluation of design variable sensitivity significantly enhances the accuracy of epidemic model parameters.
Conclusions:
- Considering design variable sensitivity in inverse problems leads to more dependable parameter estimations.
- The proposed robust methodology offers a more realistic approach to modeling infectious disease spread.
- This work provides a foundation for more accurate forecasting and intervention strategies for epidemics.
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