Formulation and Solution of an Inverse Reliability Problem to Simulate the Dynamic Behavior of COVID-19 Pandemic

Authors

  • F. S. Lobato Chemical Engineering Faculty, Federal University of Uberlândia
  • G. M. Platt School of Chemistry and Food, Federal University of Rio Grande
  • G. B. Libotte National Laboratory for Scientific Computing https://orcid.org/0000-0002-4583-6026
  • A. J. Silva Neto Polytechnic Institute, Rio de Janeiro State University

DOI:

https://doi.org/10.5540/tcam.2021.022.01.00091

Keywords:

Inverse Problem, Reliability-Based Optimization, Modeling, COVID-19

Abstract

Different types of mathematical models have been used to predict the dynamic behavior of the novel coronavirus (COVID-19). Many of them involve the formulation and solution of inverse problems. This kind of problem is generally carried out by considering the model, the vector of design variables, and system parameters as deterministic values. In this contribution, a methodology based on a double loop iteration process and devoted to evaluate the influence of uncertainties on inverse problem is evaluated. The inner optimization loop is used to find the solution associated with the highest probability value, and the outer loop is the regular optimization loop used to determine the vector of design variables. For this task, we use an inverse reliability approach and Differential Evolution algorithm. For illustration purposes, the proposed methodology is applied to estimate the parameters of SIRD (Susceptible-Infectious-Recovery-Dead) model associated with dynamic behavior of COVID-19 pandemic considering real data from China's epidemic and uncertainties in the basic reproduction number (R0). The obtained results demonstrate, as expected, that the increase of reliability implies the increase of the objective function value.

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Published

2021-04-17

How to Cite

Lobato, F. S., Platt, G. M., Libotte, G. B., & Silva Neto, A. J. (2021). Formulation and Solution of an Inverse Reliability Problem to Simulate the Dynamic Behavior of COVID-19 Pandemic. Trends in Computational and Applied Mathematics, 22(1), 91–107. https://doi.org/10.5540/tcam.2021.022.01.00091

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Original Article