Physical-mathematical modeling for decision-making against COVID-19 in Cuba

Detalhes bibliográficos
Autor(a) principal: Vargas, Héctor Eduardo Sánchez
Data de Publicação: 2020
Outros Autores: Sánchez, Luis Beltrán Ramos, Llanes, Pablo Ángel Galindo, Rodríguez, Amyrsa Salgado
Tipo de documento: preprint
Idioma: spa
Título da fonte: SciELO Preprints
Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/815
Resumo: Objective: Apply physical-mathematical modeling to the dynamics of COVID-19 for decision-making associated with the mitigation and eradication of the epidemic in Cuba. Methods: The modeling was applied to characterize the peak timing of epidemic and behavior of the epidemic, in both cases using MATLAB tools and functions. The peak timing was determined with the application of the SIR model, after some adjustments. It was adjusted with the GlobalSearch optimization strategy. For its solution, the ode23tb function was used, which uses a combined Runge-Kutta algorithm with a trapezoidal rule algorithm. For forecasting epidemic behavior, an exponential model was adjusted using the Curve Fitting tool. Main results: The parameters of the SIR model were identified with an adequate adjustment error and the forecast of the peak timing was achieved by simulation, both in date and magnitude, two weeks in advance and with satisfactory precision. For the peak date, the susceptible, accumulated infected and recovered were also predicted. The calculated basic reproduction number (R0) of 3.62 made it possible to determine that, to eradicate the epidemic by vaccination the immunized population must be greater than 72%. The calculation of the effective reproduction number (Ref) allowed evaluating the effectiveness of the mitigation measures. Reflection was made on the conduct to be followed to eradicate the epidemic. Conclusions: The SIR model demonstrated the ability to predict the peak timing of the epidemic. The R0 of the SARS-CoV-2 allowed to corroborate its high transmissibility. Mitigation measures have been effective and should be maintained until the epidemic is eradicated, even for Ref <1, as long as 72% of the population is not immunized to achieve irreversible eradication.
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spelling Physical-mathematical modeling for decision-making against COVID-19 in CubaModelación físico-matemática para la toma de decisiones frente a la COVID-19 en CubaEpidemia de la COVID-19toma de decisionesmodelación matemáticanúmero de reproducción COVID-19 epidemicdecision-makingmathematical modelingreproduction numberObjective: Apply physical-mathematical modeling to the dynamics of COVID-19 for decision-making associated with the mitigation and eradication of the epidemic in Cuba. Methods: The modeling was applied to characterize the peak timing of epidemic and behavior of the epidemic, in both cases using MATLAB tools and functions. The peak timing was determined with the application of the SIR model, after some adjustments. It was adjusted with the GlobalSearch optimization strategy. For its solution, the ode23tb function was used, which uses a combined Runge-Kutta algorithm with a trapezoidal rule algorithm. For forecasting epidemic behavior, an exponential model was adjusted using the Curve Fitting tool. Main results: The parameters of the SIR model were identified with an adequate adjustment error and the forecast of the peak timing was achieved by simulation, both in date and magnitude, two weeks in advance and with satisfactory precision. For the peak date, the susceptible, accumulated infected and recovered were also predicted. The calculated basic reproduction number (R0) of 3.62 made it possible to determine that, to eradicate the epidemic by vaccination the immunized population must be greater than 72%. The calculation of the effective reproduction number (Ref) allowed evaluating the effectiveness of the mitigation measures. Reflection was made on the conduct to be followed to eradicate the epidemic. Conclusions: The SIR model demonstrated the ability to predict the peak timing of the epidemic. The R0 of the SARS-CoV-2 allowed to corroborate its high transmissibility. Mitigation measures have been effective and should be maintained until the epidemic is eradicated, even for Ref <1, as long as 72% of the population is not immunized to achieve irreversible eradication.Objetivo: Aplicar la modelación físico-matemática a la dinámica de la COVID-19 para la toma de decisiones asociadas a la mitigación y erradicación de la epidemia en Cuba. Métodos: La modelación se aplicó para la caracterización del pronóstico del pico y el comportamiento reproductivo de la epidemia, en ambos casos usando herramientas y funciones de MATLAB. El pico se determinó con la aplicación del modelo SIR, luego de algunas adecuaciones. Este se ajustó con la estrategia de optimización GlobalSearch. Para su solución se empleó la función ode23tb que usa un algoritmo combinado de Runge-Kutta con otro de regla trapezoidal. Para el comportamiento reproductivo se realizó el ajuste de un modelo exponencial empleando la herramienta Curve Fitting.  Principales resultados: Se identificaron los parámetros del modelo SIR con un error de ajuste adecuado y por simulación se logró el pronóstico del pico, tanto en fecha como envergadura, con dos semanas de anticipación y con una precisión satisfactoria. Para la fecha del pico, se pronosticaron igualmente los susceptibles, infectados acumulados y recuperados. El número de reproducción básico (R0) calculado de 3,62 permitió determinar que, para erradicar la epidemia por vacunación, la población inmunizada debe ser superior al 72 %. El cálculo del número de reproducción efectivo (Ref) permitió evaluar la eficacia de las medidas de mitigación. Se reflexionó sobre la conducta a seguir para erradicar la epidemia. Conclusiones: El modelo SIR demostró capacidad para predecir el pico de la epidemia. El R0 del SARS-CoV-2 permitió corroborar su elevada transmisibilidad. Las medidas de mitigación han sido efectivas y deben mantenerse hasta erradicar la epidemia, incluso para Ref <1, mientras no se inmunice el 72 % de la población para lograr una erradicación irreversible.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-06-23info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/81510.1590/SciELOPreprints.815spahttps://preprints.scielo.org/index.php/scielo/article/view/815/1112Copyright (c) 2020 Héctor Eduardo Sánchez Vargas, Luis Beltrán Ramos Sánchez, Pablo Ángel Galindo Llanes, Amyrsa Salgado Rodríguezhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessVargas, Héctor Eduardo Sánchez Sánchez, Luis Beltrán RamosLlanes, Pablo Ángel GalindoRodríguez, Amyrsa Salgadoreponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-06-19T14:30:34Zoai:ops.preprints.scielo.org:preprint/815Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-06-19T14:30:34SciELO Preprints - SciELOfalse
dc.title.none.fl_str_mv Physical-mathematical modeling for decision-making against COVID-19 in Cuba
Modelación físico-matemática para la toma de decisiones frente a la COVID-19 en Cuba
title Physical-mathematical modeling for decision-making against COVID-19 in Cuba
spellingShingle Physical-mathematical modeling for decision-making against COVID-19 in Cuba
Vargas, Héctor Eduardo Sánchez
Epidemia de la COVID-19
toma de decisiones
modelación matemática
número de reproducción
COVID-19 epidemic
decision-making
mathematical modeling
reproduction number
title_short Physical-mathematical modeling for decision-making against COVID-19 in Cuba
title_full Physical-mathematical modeling for decision-making against COVID-19 in Cuba
title_fullStr Physical-mathematical modeling for decision-making against COVID-19 in Cuba
title_full_unstemmed Physical-mathematical modeling for decision-making against COVID-19 in Cuba
title_sort Physical-mathematical modeling for decision-making against COVID-19 in Cuba
author Vargas, Héctor Eduardo Sánchez
author_facet Vargas, Héctor Eduardo Sánchez
Sánchez, Luis Beltrán Ramos
Llanes, Pablo Ángel Galindo
Rodríguez, Amyrsa Salgado
author_role author
author2 Sánchez, Luis Beltrán Ramos
Llanes, Pablo Ángel Galindo
Rodríguez, Amyrsa Salgado
author2_role author
author
author
dc.contributor.author.fl_str_mv Vargas, Héctor Eduardo Sánchez
Sánchez, Luis Beltrán Ramos
Llanes, Pablo Ángel Galindo
Rodríguez, Amyrsa Salgado
dc.subject.por.fl_str_mv Epidemia de la COVID-19
toma de decisiones
modelación matemática
número de reproducción
COVID-19 epidemic
decision-making
mathematical modeling
reproduction number
topic Epidemia de la COVID-19
toma de decisiones
modelación matemática
número de reproducción
COVID-19 epidemic
decision-making
mathematical modeling
reproduction number
description Objective: Apply physical-mathematical modeling to the dynamics of COVID-19 for decision-making associated with the mitigation and eradication of the epidemic in Cuba. Methods: The modeling was applied to characterize the peak timing of epidemic and behavior of the epidemic, in both cases using MATLAB tools and functions. The peak timing was determined with the application of the SIR model, after some adjustments. It was adjusted with the GlobalSearch optimization strategy. For its solution, the ode23tb function was used, which uses a combined Runge-Kutta algorithm with a trapezoidal rule algorithm. For forecasting epidemic behavior, an exponential model was adjusted using the Curve Fitting tool. Main results: The parameters of the SIR model were identified with an adequate adjustment error and the forecast of the peak timing was achieved by simulation, both in date and magnitude, two weeks in advance and with satisfactory precision. For the peak date, the susceptible, accumulated infected and recovered were also predicted. The calculated basic reproduction number (R0) of 3.62 made it possible to determine that, to eradicate the epidemic by vaccination the immunized population must be greater than 72%. The calculation of the effective reproduction number (Ref) allowed evaluating the effectiveness of the mitigation measures. Reflection was made on the conduct to be followed to eradicate the epidemic. Conclusions: The SIR model demonstrated the ability to predict the peak timing of the epidemic. The R0 of the SARS-CoV-2 allowed to corroborate its high transmissibility. Mitigation measures have been effective and should be maintained until the epidemic is eradicated, even for Ref <1, as long as 72% of the population is not immunized to achieve irreversible eradication.
publishDate 2020
dc.date.none.fl_str_mv 2020-06-23
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10.1590/SciELOPreprints.815
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identifier_str_mv 10.1590/SciELOPreprints.815
dc.language.iso.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://preprints.scielo.org/index.php/scielo/article/view/815/1112
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SciELO Preprints
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SciELO Preprints
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