Physical-mathematical modeling for decision-making against COVID-19 in Cuba
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
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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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 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/preprint info:eu-repo/semantics/publishedVersion |
format |
preprint |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://preprints.scielo.org/index.php/scielo/preprint/view/815 10.1590/SciELOPreprints.815 |
url |
https://preprints.scielo.org/index.php/scielo/preprint/view/815 |
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 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
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application/pdf |
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SciELO Preprints SciELO Preprints SciELO Preprints |
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SciELO Preprints SciELO Preprints SciELO Preprints |
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SciELO Preprints - SciELO |
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