Exploring the roles of local mobility patterns, socioeconomic conditions, and lockdown policies in shaping the patterns of COVID-19 spread
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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 pandemic development, we aim to understand and explain [make sense of] the observed heterogeneous regional patterns of contagion across time and space. We will also consider the causal effects produced by confinement policies by developing data-based models to explore, simulate, and estimate these policies' effectiveness. We intend to develop a methodology to assess and improve public policies' effectiveness associated with the fight against the pandemic, emphasizing its convenience, the precise time of its application, and extension. The contributions of this work can be used regardless of the region. The only likely impediment is the availability of the appropriate data.
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