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20241108120331.0 |
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241023s2023 bl d grm ||||||eng d |
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|a Sistema de Bibliotecas de Universidad de Costa Rica
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|a TFG 49094
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|a Loria Sorio, Jennifer
|d 1991-
|e Autor/a
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|a Epidemiological models :
|b extensions, applications and calibration /
|c Jennifer Loria Sorio ; asesor Jorge Passamani Zubelli.
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|a [Rio de Janeiro, Brasil],
|c 2023.
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|a 112 páginas :
|b diagramas a color, gráficos a color.
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|a La función del director fue determinada por el catalogador
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|a Thesis (doctor of philosofy)--Instituto de Matemática Pura e Aplicada, 2024
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|a We present, analyze, and calibrate an extended version of the susceptible-exposed-infected-removedlike (SEIR-like) model. We also apply the proposed model in different contexts using real data. We assume that the transmission parameter β assumes a functional form, which is fundamental to incorporating regime changes in the pathogen spread dynamic. Under this setting, we solve the inverse problem associated with the rate β, providing theoretical results on the regularity of the so-called direct problem. In other words, we show the parameter-to-solution map's injectivity, continuity, and differentiability. Based on these results, we apply Tikhonov-type regularization to solve the inverse problem, showing the existence and stability of approximate solutions, as well as their convergence to the solution of the inverse problem. We present a novel methodology to estimate the stable rates of hospitalization and death related to the Corona Virus Disease 2019 (COVID-19) using publicly available reports from different places. These rates were then used to estimate the true number of infections achieving a remarkably close agreement with seroprevalence studies. These results were used to assess the impact of underreporting infections on vaccination strategies. The vaccination scenarios were designed using the proposed SEIRLike model. These results are published in [1]. We also evaluate how the estimated vaccine efficacy (VE) can vary depending on the design of the Clinical Trial (CT) when the force of infection is time dependent. Using a simple mathematical model, we tested the hypothesis VE is sensitive to the difference between the moment the CT begins and the peak in the outbreak intensity. This methodology is tested using real data obtained in a CT designed to estimate the efficacy of one of the vaccines against the new coronavirus SARS-CoV-2.
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|a Resumen también en portugués
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|a Se considera un trabajo Final de Graduación del Sistema de Estudios de Posgrado, según oficio no. OAICE-1276-2024
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|a EPIDEMIOLOGIA
|x MODELOS MATEMATICOS
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|a REGRESIÓN RIDGE (ESTADÍSTICA)
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| 650 |
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|a VACUNAS CONTRA LA COVID-19
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|a Passamani Zubelli, Jorge
|e Director/a del TFG
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|a OAICE
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|a 2024-O
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|a tesis doctoral
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| 916 |
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|a Centro Catalográfico
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| 949 |
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|a ACC -JTG
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