A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of Mexico
More than two years after the declaration of the COVID-19 pandemic, we are still experiencing contagious waves. As this is a long-lasting process, it becomes relevant to have a predictive tool to identify the intensively active places within a region. This study presents the development of a forecas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/10/5/138 |
| _version_ | 1797469811807092736 |
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| author | Gerardo L. Febres Carlos Gershenson |
| author_facet | Gerardo L. Febres Carlos Gershenson |
| author_sort | Gerardo L. Febres |
| collection | DOAJ |
| description | More than two years after the declaration of the COVID-19 pandemic, we are still experiencing contagious waves. As this is a long-lasting process, it becomes relevant to have a predictive tool to identify the intensively active places within a region. This study presents the development of a forecasting model applied to foresee the progress of the contagious process in Mexico and its regions. The method comprehends aspects of deterministic and probabilistic modeling. The deterministic part comprises the classical SIR model with some adjustments. The probabilistic part builds and populates a three-dimensional array, which is then used to describe and recall the probabilities of going from one status to another after some time, very much like a Markovian process. The process status is modeled as the combination of two conditions: the infection exponential growth parameter and a proxy variable we named “permissiveness” that accounts for all combined social activity factors affecting COVID-19 propagation. The results offer projections of the exponential growth parameter and the number of newly infected individuals for three weeks into the future. The proposed method’s capabilities allow for predicting newly COVID-19-infected individuals with reasonable precision while capturing the characteristic dynamics and behavior of the modeled system. |
| first_indexed | 2024-03-09T19:26:20Z |
| format | Article |
| id | doaj.art-e94dc778b697421b8b91596996296942 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-09T19:26:20Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-e94dc778b697421b8b915969962969422023-11-24T02:54:48ZengMDPI AGSystems2079-89542022-09-0110513810.3390/systems10050138A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of MexicoGerardo L. Febres0Carlos Gershenson1Departamento de Procesos y Sistemas, Universidad Simón Bolívar, Sartenejas, Baruta 1080, Miranda, VenezuelaCentro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City 04510, MexicoMore than two years after the declaration of the COVID-19 pandemic, we are still experiencing contagious waves. As this is a long-lasting process, it becomes relevant to have a predictive tool to identify the intensively active places within a region. This study presents the development of a forecasting model applied to foresee the progress of the contagious process in Mexico and its regions. The method comprehends aspects of deterministic and probabilistic modeling. The deterministic part comprises the classical SIR model with some adjustments. The probabilistic part builds and populates a three-dimensional array, which is then used to describe and recall the probabilities of going from one status to another after some time, very much like a Markovian process. The process status is modeled as the combination of two conditions: the infection exponential growth parameter and a proxy variable we named “permissiveness” that accounts for all combined social activity factors affecting COVID-19 propagation. The results offer projections of the exponential growth parameter and the number of newly infected individuals for three weeks into the future. The proposed method’s capabilities allow for predicting newly COVID-19-infected individuals with reasonable precision while capturing the characteristic dynamics and behavior of the modeled system.https://www.mdpi.com/2079-8954/10/5/138COVID-19SIR modelinfectious diseasesforecasting methodssystem dynamics pattern |
| spellingShingle | Gerardo L. Febres Carlos Gershenson A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of Mexico Systems COVID-19 SIR model infectious diseases forecasting methods system dynamics pattern |
| title | A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of Mexico |
| title_full | A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of Mexico |
| title_fullStr | A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of Mexico |
| title_full_unstemmed | A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of Mexico |
| title_short | A Deterministic–Statistical Hybrid Forecast Model: The Future of the COVID-19 Contagious Process in Several Regions of Mexico |
| title_sort | deterministic statistical hybrid forecast model the future of the covid 19 contagious process in several regions of mexico |
| topic | COVID-19 SIR model infectious diseases forecasting methods system dynamics pattern |
| url | https://www.mdpi.com/2079-8954/10/5/138 |
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