Regression model for the reported infected during emerging pandemics under the stochastic SEIR.
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Date
2023
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Abstract
The COVID-19 pandemic revealed the necessity of measuring the statistical relationship
between the transmission rate of epidemic diseases and the social/behavioral, logistical,
and economic variables of the affected region. This paper introduces a regression model
to estimate the impact of such covariates on the infectious rate of epidemiological agents.
Hidden logistical predictor components, such as weekly seasonality of reported data, can
also be accessed with the proposed methodology. For this, we assume that the dynamics
of officially reported data of emerging pandemics, related to infected groups, follows a
stochastic SEIR model. The main advantage of our method is that it is based on a new three-
step algorithm that combines the classical likelihood principle, the minimization of the mean
squared error, and a tri-section algorithm to estimate, simultaneously, the coefficients of the
covariates and the parameters of the compartmental model. Simulation studies are provided
to certify the accuracy of the proposed inference methodology. The model is further applied
to analyze the official statistical reports of COVID-19 data in the state of São Paulo, Brazil.
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Keywords
COVID-19, Social isolation
Citation
SILVA, I. R. et al. Regression model for the reported infected during emerging pandemics under the stochastic SEIR. Computational and Applied Mathematics, v. 42, n. 96, 2023. Disponível em: <https://link.springer.com/article/10.1007/s40314-023-02241-w>. Acesso em: 06 jul. 2023.