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Vol. 32 No. 66 (2023)
October-December 2023 (Continuous Publication)

Papers

Discovering the Spread Patterns of SARS-CoV-2 in Metropolitan Areas

Published 2023-12-31

  • Mauricio-René Herrera-Marín
    • ,
  • Francisco Vergara-Perucich
    • ,
  • Carlos Aguirre-Núñez
    • ,
  • Alex Godoy-Faúndez

Mauricio-René Herrera-Marín
Universidad del Desarrollo

Francisco Vergara-Perucich
Universidad de Las Américas

Carlos Aguirre-Núñez
Universidad de Las Américas

Alex Godoy-Faúndez
Universidad del Desarrollo

Abstract

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.

Keywords

Time-varying regression models, City dynamics, COVID-19 spread dynamics, elastic-net regularization, socio-spatial mobility indicators

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