Licenciatura em Matemática (Sede)

URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/24

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Autor: search.filters.author.Santos, Letícia Maria Menezes dosAssunto: search.filters.subject.COVID-19, Pandemia de

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    Modelos matemáticos epidemiológicos do tipo SIS e SIR e o segundo método de Lyapunov
    (2023-05-05) Santos, Letícia Maria Menezes dos; Didier, Maria Ângela Caldas; Freitas, Lorena Brizza Soares; http://lattes.cnpq.br/2302580820419163; http://lattes.cnpq.br/9721552594807972; http://lattes.cnpq.br/9115322351374062
    This work aims to study epidemiological mathematical models of the SIS (Susceptible- Infected-Susceptible) and SIR (Susceptible-Infected-Removed) types, focusing on the stability of the equilibrium points of the differential equation systems that describe them. Stability analysis will be presented in two ways, using the eigenvalue characteristics and/or the trace of the system matrix and using the Second Lyapunov Method. We also address the stability of variations of these models, considering non-constant total population and vital dynamics (births and deaths), or dividing the population of infected individuals into exposed individuals (infected who do not yet transmit the disease) and infectious individuals (infected who transmit the disease). We define the Basic Reproduction Value, and for some models, we present ways to obtain it from the involved rates and initial conditions of the system. A calculation that determines the maximum number of infected individuals reached was performed for the SIR model with constant total population and without vital dynamics. Finally, to understand how these models are practically applied, we decided to study the evolution of the COVID-19 pandemic in the state of Pernambuco in 2020 and 2021 through the SIR model with constant population size and no vital dynamics. To do this, we calculated the Basic Reproduction Value and the maximum number of infected individuals for each case. It is worth noting that an evolutionary algorithm was used to obtain a model that best approximated the real data.