Exploring the roles of local mobility patterns, socioeconomic conditions, and lockdown policies in shaping the patterns of COVID-19 spread
Mauricio Herrera

TL;DR
This paper investigates how local mobility, socioeconomic factors, and lockdown policies influence COVID-19 spread patterns using data-driven models, aiming to enhance policy effectiveness assessment across regions.
Contribution
It develops a methodology integrating mobility, socioeconomic, and COVID-19 data to analyze and simulate the effects of confinement policies on pandemic dynamics.
Findings
Identifies key factors affecting regional COVID-19 spread.
Provides a data-based framework for policy impact simulation.
Offers insights applicable to various regions with data availability.
Abstract
The COVID-19 crisis has shown that we can only prevent the risk of mass contagion through timely, large-scale, coordinated, and decisive actions. However, frequently the models used by experts [from whom decision-makers get their main advice] focus on a single perspective [for example, the epidemiological one] and do not consider many of the multiple forces that affect the COVID-19 outbreak patterns. The epidemiological, socioeconomic, and human mobility context of COVID-19 can be considered as a complex adaptive system. So, these interventions (for example, lock-downs) could have many and/or unexpected ramifications. This situation makes it difficult to understand the overall effect produced by any public policy measure and, therefore, to assess its real effectiveness and convenience. By using mobile phone data, socioeconomic data, and COVID-19 cases data recorded throughout the…
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