Selection of models and parameter estimation for monitoring the COVID-19 epidemic in Brazil via Bayesian inference

Authors

DOI:

https://doi.org/10.5902/2179460X73812

Keywords:

Approximate Bayesian Computation, Epidemiological models, COVID-19

Abstract

In 2019, a new strain of coronavirus led to an outbreak of disease cases named COVID-19, evolving rapidly into a pandemic. In Brazil, delayed decision making and lack of knowledge have resulted in an alarming increase in daily transmission and deaths. In this context, researchers used mathematical models to assist in determining the parameters that act in the spread of diseases, revealing containment measures. However, numerous mathematical models exist in the literature, each with specific parameters to be specified, leading to an important question about which model best represents the pandemic behavior. In this regard, this work aims to apply the Approximate Bayesian Computation method to select the best model and simultaneously estimate the parameters to resolve the abovementioned issue. The models adopted were susceptible-infected-recovered (SIR), susceptible-exposed-infected-recovered (SEIR), susceptible-infected-recovered-susceptible (SIRS), and susceptible-exposed-infected-recovered-susceptible (SEIRS). Approximate Bayesian Computation Monte Carlo Sequencing (ABC-SMC) was used to select among four competing models to represent the number of infected individuals and to estimate the model parameters based on three periods of Brazil COVID-19 data. A forecasting test was performed to test the ABC-SMC algorithm and the selected models for two months. The result was compared with the actual number of infected that were reported. Among the teste models, it was found that the ABC-SMC algorithm had a promising performance, since the data were noisy and the models could not predict all parameters.

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Author Biographies

Lucas Martins Inez, Federal University of Espírito Santo

He graduated in Biochemistry from the Federal University of São João Del Rei. Bachelor's degree in Chemical Engineering from the Federal University of Espírito Santo. Currently pursuing a Master's degree in Chemical Engineering at the Federal University of Viçosa. Research projects focused on process optimization and feasibility analysis. Data engineering professional at MD2 Consultancy.

 

Carlos Eduardo Rambalducci Dalla, Federal University of Espírito Santo

He graduated in Chemical Engineering in 2017 and received his master's degree in 2019, all from the Department of Chemical Engineering at the Federal Rural University of Rio de Janeiro (UFRRJ). Currently, Ph.D. student in Mechanical Engineering at Federal University of Espirito Santo where research activity is focused mainly on irreversible processes, thermodynamics, inverse problems, and applied numerical methods.

Wellington Betencurte da Silva, Fluminense Federal University

He is graduated in Mathematics from Universidade Federal Fluminense, a master's in Mechanical Engineering from Instituto Militar de Engenharia, and a doctorate in Mechanical Engineering from Federal University of Rio de Janeiro-UFRJ with a co-guardianship period in Ecole des Mines d'Albi-Carmaux. Has experience in Applied Mathematics, acting on the following subjects: numerical methods, Bayesian filters, optimization and heat transfer.

Julio Cesar Sampaio Dutra, Federal University of Espírito Santo

He is Professor at Department of Rural Engineering at the Federal University of Espírito Santo (UFES). He holds a Bachelor's degree in Chemical Engineering from UFRRJ (2006) and a Ph.D. in Chemical Engineering from COPPE/UFRJ (2012), with a research period at NTNU, Norway (2011). His main research interests encompass Mathematical Modeling, Process Monitoring and Control.

José Mir Justino da Costa, Federal University of Amazonas

He is an associate professor at the Federal University of Amazonas (UFAM). He holds a degree in mathematics, a master's degree in mathematics from UFAM, and a doctorate in Mechanical Engineering from the Federal University of Rio de Janeiro -UFRJ. Has experience in applied Probability and Statistics and growth modeling tumor through Bayesian Inference and applied Probability and Statistics, as well as growth modelling tumor through Bayesian Inference.

 

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Published

2023-12-01

How to Cite

Inez, L. M., Dalla, C. E. R., Silva, W. B. da, Dutra, J. C. S., & Costa, J. M. J. da. (2023). Selection of models and parameter estimation for monitoring the COVID-19 epidemic in Brazil via Bayesian inference. Ciência E Natura, 45(esp. 3), e73812. https://doi.org/10.5902/2179460X73812

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Vol. 45 No. esp. 3 (2023): ENMC/ECTM/MCSul/SEMENGO

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Special Edition

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