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- W4317395170 abstract "The outbreak of COVID-19 led the world to an extreme health and economic cataclysm. As the epidemic stays the course, the researchers worldwide attempt to study its features and to discover a panacea. In this paper we implement a SEIR epidemic model to investigate the disease dynamics. We reduce the classical formulation to a system of two first-order ordinary differential equations. The inverse problem is defined in a minimization setting and solved via numerical optimization techniques. We discuss how the value of the basic reproduction number determines the epidemic outcome as well as the economic impact of the disease spread and the imposed restriction measures. To illustrate the findings, we provide ample numerical experiments and simulations with realistic data to conclude the proposed algorithm is capable of identifying the dynamics parameters in an efficient and robust manner. The paper is concluded with a discussion concerning the applicability of the approach." @default.
- W4317395170 created "2023-01-19" @default.
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- W4317395170 date "2023-01-01" @default.
- W4317395170 modified "2023-10-18" @default.
- W4317395170 title "Coefficient Identification for SEIR Model and Economic Forecasting in the Propagation of COVID-19" @default.
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- W4317395170 doi "https://doi.org/10.1007/978-3-031-20951-2_4" @default.
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