Un Modelo Matemático SIR-D Segmentado para la Dinámica de Propagación del Coronavirus (COVID-19) en el Perú
The present study proposes the use of a segmented SIR-D mathematical model to predict the evolution of epidemiological populations of interest in the COVID-19 pandemic (Susceptible [S], Infected [I], Recovered [R] and dead [D]), information that is often key to guiding decision-making in the fight a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Spanish |
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Universidad Nacional de Trujillo
2020-07-01
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| Series: | Selecciones Matemáticas |
| Subjects: | |
| Online Access: | https://revistas.unitru.edu.pe/index.php/SSMM/article/view/2970 |
| _version_ | 1818573525424799744 |
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| author | Neisser Pino Romero Percy Soto-Becerra Ricardo Angelo Quispe Mendizábal |
| author_facet | Neisser Pino Romero Percy Soto-Becerra Ricardo Angelo Quispe Mendizábal |
| author_sort | Neisser Pino Romero |
| collection | DOAJ |
| description | The present study proposes the use of a segmented SIR-D mathematical model to predict the evolution of epidemiological populations of interest in the COVID-19 pandemic (Susceptible [S], Infected [I], Recovered [R] and dead [D]), information that is often key to guiding decision-making in the fight against epidemics. In order to obtain a better model calibration and a lower prediction error in the short term, we performed the model segmentation in 6 stages of periods of 14 days each. At each stage, the epidemiological that define the system of equations are empirically estimated by linear regression of the epidemiological surveillance data that the Peruvian Ministry of Health collects and reports daily. This strategy showed better model calibration compared to an unsegmented SIR-D model. |
| first_indexed | 2024-12-15T00:12:30Z |
| format | Article |
| id | doaj.art-89635fbf2d0f46fcafc052b52a643c79 |
| institution | Directory Open Access Journal |
| issn | 2411-1783 2411-1783 |
| language | Spanish |
| last_indexed | 2024-12-15T00:12:30Z |
| publishDate | 2020-07-01 |
| publisher | Universidad Nacional de Trujillo |
| record_format | Article |
| series | Selecciones Matemáticas |
| spelling | doaj.art-89635fbf2d0f46fcafc052b52a643c792022-12-21T22:42:32ZspaUniversidad Nacional de TrujilloSelecciones Matemáticas2411-17832411-17832020-07-0170116217110.17268/sel.mat.2020.01.15Un Modelo Matemático SIR-D Segmentado para la Dinámica de Propagación del Coronavirus (COVID-19) en el PerúNeisser Pino Romero0https://orcid.org/0000-0002-9865-5974Percy Soto-Becerra1https://orcid.org/0000-0001-5332-9254Ricardo Angelo Quispe Mendizábal2https://orcid.org/0000-0001-6328-3093Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia. Lima, PerúInstituto de Evaluación de Tecnologías en Salud e Investigación - IETSI, EsSalud. Lima, PerúFacultad de Ciencias Fisicas, Universidad Nacional Mayor de San Marcos. Lima, PerúThe present study proposes the use of a segmented SIR-D mathematical model to predict the evolution of epidemiological populations of interest in the COVID-19 pandemic (Susceptible [S], Infected [I], Recovered [R] and dead [D]), information that is often key to guiding decision-making in the fight against epidemics. In order to obtain a better model calibration and a lower prediction error in the short term, we performed the model segmentation in 6 stages of periods of 14 days each. At each stage, the epidemiological that define the system of equations are empirically estimated by linear regression of the epidemiological surveillance data that the Peruvian Ministry of Health collects and reports daily. This strategy showed better model calibration compared to an unsegmented SIR-D model.https://revistas.unitru.edu.pe/index.php/SSMM/article/view/2970coronavirus (covid-19)epidemiologyordinary differential equationscomputational simulationregression methods |
| spellingShingle | Neisser Pino Romero Percy Soto-Becerra Ricardo Angelo Quispe Mendizábal Un Modelo Matemático SIR-D Segmentado para la Dinámica de Propagación del Coronavirus (COVID-19) en el Perú Selecciones Matemáticas coronavirus (covid-19) epidemiology ordinary differential equations computational simulation regression methods |
| title | Un Modelo Matemático SIR-D Segmentado para la Dinámica de Propagación del Coronavirus (COVID-19) en el Perú |
| title_full | Un Modelo Matemático SIR-D Segmentado para la Dinámica de Propagación del Coronavirus (COVID-19) en el Perú |
| title_fullStr | Un Modelo Matemático SIR-D Segmentado para la Dinámica de Propagación del Coronavirus (COVID-19) en el Perú |
| title_full_unstemmed | Un Modelo Matemático SIR-D Segmentado para la Dinámica de Propagación del Coronavirus (COVID-19) en el Perú |
| title_short | Un Modelo Matemático SIR-D Segmentado para la Dinámica de Propagación del Coronavirus (COVID-19) en el Perú |
| title_sort | un modelo matematico sir d segmentado para la dinamica de propagacion del coronavirus covid 19 en el peru |
| topic | coronavirus (covid-19) epidemiology ordinary differential equations computational simulation regression methods |
| url | https://revistas.unitru.edu.pe/index.php/SSMM/article/view/2970 |
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