Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas
The spread of COVID-19 has been extensively studied, but the intricate dynamics of its transmission in interdependent and segregated urban areas, constrained by mobility restrictions, have not been completely understood yet. The pandemic's dynamic-adaptive nature implies that virus spread is in...
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| Format: | Online |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2023
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16457 |
| _version_ | 1801706104438128640 |
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| author | Herrera-Marín, Mauricio-René Vergara-Perucich, Francisco Aguirre-Núñez, Carlos Godoy-Faúndez, Alex |
| author_facet | Herrera-Marín, Mauricio-René Vergara-Perucich, Francisco Aguirre-Núñez, Carlos Godoy-Faúndez, Alex |
| author_sort | Herrera-Marín, Mauricio-René |
| collection | OJS |
| description | The spread of COVID-19 has been extensively studied, but the intricate dynamics of its transmission in interdependent and segregated urban areas, constrained by mobility restrictions, have not been completely understood yet. The pandemic's dynamic-adaptive nature implies that virus spread is influenced by diverse factors operating disparately in urban areas with distinct roles. This study investigates the dynamic spread patterns of COVID-19 in the Santiago Metropolitan Area (SMA), Chile, leveraging explanatory variables related to urban mobility, socio-spatial characteristics, segregation, and sanitary measures. Using publicly available mobility data, we used two indices—the Internal Mobility Index (capturing individual trips within a city’s commune), and the External Mobility Index (indicating trips crossing commune borders). These indices were derived from geolocation data recorded by the cellular telephone antenna network of the Telefónica company by tracking successive antenna transitions during trips. The analysis encompasses a three-stage pandemic pattern, corresponding to periods before, during, and after an initial lockdown in the pandemic's first year. Elastic-Net-Penalty regression models, skillful in both feature selection and managing highly correlated predictors while maintaining the interpretability of the models, are used. These models employ a combination of L1 (ridge) and L2 (lasso) regularized log-likelihood optimization. The ridge penalty functions by contracting the coefficients of correlated predictors, pulling them closer to each other. In contrast, the lasso method tends to choose one predictor and exclude the others. The analysis with these models unveils influences of various explanatory variable subsets throughout the pandemic. Importantly, the study provides evidence justifying the suboptimal outcomes of the dynamic quarantine imposed by authorities. Mobility restrictions were implemented without considering the intricate contextual factors, thus impacting vulnerable areas of the city adversely. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-16457 |
| institution | Revista Facultad de Ingeniería |
| language | eng |
| publishDate | 2023 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-164572024-02-23T14:02:38Z Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas Descubriendo patrones de propagación del SARS-CoV-2 en áreas metropolitanas Herrera-Marín, Mauricio-René Vergara-Perucich, Francisco Aguirre-Núñez, Carlos Godoy-Faúndez, Alex Time-varying regression models City dynamics COVID-19 spread dynamics elastic-net regularization socio-spatial mobility indicators dinámica de propagación COVID-19 dinámica urbana indicadores de movilidad regresión variable en el tiempo regularización elastic-net The spread of COVID-19 has been extensively studied, but the intricate dynamics of its transmission in interdependent and segregated urban areas, constrained by mobility restrictions, have not been completely understood yet. The pandemic's dynamic-adaptive nature implies that virus spread is influenced by diverse factors operating disparately in urban areas with distinct roles. This study investigates the dynamic spread patterns of COVID-19 in the Santiago Metropolitan Area (SMA), Chile, leveraging explanatory variables related to urban mobility, socio-spatial characteristics, segregation, and sanitary measures. Using publicly available mobility data, we used two indices—the Internal Mobility Index (capturing individual trips within a city’s commune), and the External Mobility Index (indicating trips crossing commune borders). These indices were derived from geolocation data recorded by the cellular telephone antenna network of the Telefónica company by tracking successive antenna transitions during trips. The analysis encompasses a three-stage pandemic pattern, corresponding to periods before, during, and after an initial lockdown in the pandemic's first year. Elastic-Net-Penalty regression models, skillful in both feature selection and managing highly correlated predictors while maintaining the interpretability of the models, are used. These models employ a combination of L1 (ridge) and L2 (lasso) regularized log-likelihood optimization. The ridge penalty functions by contracting the coefficients of correlated predictors, pulling them closer to each other. In contrast, the lasso method tends to choose one predictor and exclude the others. The analysis with these models unveils influences of various explanatory variable subsets throughout the pandemic. Importantly, the study provides evidence justifying the suboptimal outcomes of the dynamic quarantine imposed by authorities. Mobility restrictions were implemented without considering the intricate contextual factors, thus impacting vulnerable areas of the city adversely. La propagación del COVID-19 ha sido extensamente estudiada, pero las dinámicas intrincadas de su transmisión en áreas urbanas interdependientes y segregadas, limitadas por restricciones de movilidad, aún no se comprenden completamente. La naturaleza dinámica y adaptativa de la pandemia implica que la propagación del virus está influenciada por diversos factores que operan de manera dispar en áreas urbanas con roles distintos. Este estudio investiga los patrones dinámicos de propagación de COVID-19 en el Área Metropolitana de Santiago (AMS), Chile, utilizando variables explicativas relacionadas con la movilidad urbana, características socioespaciales, segregación y medidas sanitarias. Utilizando datos de movilidad públicos, se emplearon dos índices: el Índice de Movilidad Interna (que captura viajes individuales dentro de una comuna de la ciudad) y el Índice de Movilidad Externa (que indica viajes que cruzan las fronteras de la comuna). Estos índices se derivaron de datos de geolocalización registrados por la red de antenas de telefonía celular de la empresa Telefónica, que rastrea transiciones sucesivas de antenas durante los viajes. El análisis abarca un patrón pandémico de tres etapas correspondientes a los períodos antes, durante y después de un confinamiento inicial en el primer año de la pandemia. Se utilizaron modelos de regresión con penalización Elastic-Net, que permiten seleccionar características y gestionar predictores altamente correlacionados, manteniendo al mismo tiempo la interpretabilidad de los modelos. Estos emplean una combinación de regularización L1 (Ridge) y L2 (Lasso) en la optimización de la verosimilitud. La penalización Ridge contrae los coeficientes de predictores correlacionados entre sí, mientras que la penalización Lasso tiende a seleccionar un subconjunto de coeficientes, asociados a predictores específicos, y descartar los demás. El análisis revela influencias distintas de varios subconjuntos de variables explicativas a lo largo de la pandemia. Es importante destacar que el estudio proporciona evidencia que justifica los resultados subóptimos de la cuarentena dinámica impuesta por las autoridades. Las restricciones de movilidad se implementaron sin tener en cuenta adecuadamente los factores contextuales intrincados, afectando negativamente a áreas vulnerables de la ciudad. Universidad Pedagógica y Tecnológica de Colombia 2023-12-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16457 Revista Facultad de Ingeniería; Vol. 32 No. 66 (2023): October-December 2023 (Continuous Publication); e16457 Revista Facultad de Ingeniería; Vol. 32 Núm. 66 (2023): Octubre-Diciembre 2023 (Publicación Continua) ; e16457 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16457/13833 Copyright (c) 2023 Mauricio-René Herrera-Marín, Francisco Vergara-Perucich, Carlos Aguirre-Núñez, Alex Godoy-Faúndez http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | Time-varying regression models City dynamics COVID-19 spread dynamics elastic-net regularization socio-spatial mobility indicators dinámica de propagación COVID-19 dinámica urbana indicadores de movilidad regresión variable en el tiempo regularización elastic-net Herrera-Marín, Mauricio-René Vergara-Perucich, Francisco Aguirre-Núñez, Carlos Godoy-Faúndez, Alex Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas |
| title | Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas |
| title_alt | Descubriendo patrones de propagación del SARS-CoV-2 en áreas metropolitanas |
| title_full | Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas |
| title_fullStr | Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas |
| title_full_unstemmed | Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas |
| title_short | Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas |
| title_sort | discovering the spread patterns of sars cov 2 in metropolitan areas |
| topic | Time-varying regression models City dynamics COVID-19 spread dynamics elastic-net regularization socio-spatial mobility indicators dinámica de propagación COVID-19 dinámica urbana indicadores de movilidad regresión variable en el tiempo regularización elastic-net |
| topic_facet | Time-varying regression models City dynamics COVID-19 spread dynamics elastic-net regularization socio-spatial mobility indicators dinámica de propagación COVID-19 dinámica urbana indicadores de movilidad regresión variable en el tiempo regularización elastic-net |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/16457 |
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