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The COVID-19 crisis has shown that we can only prevent the risk of masscontagion through timely, large-scale, coordinated, and decisive actions.However, frequently the models used by experts [from whom decision-makers gettheir main advice] focus on a single perspective [for example, theepidemiological one] and do not consider many of the multiple forces thataffect the COVID-19 outbreak patterns. The epidemiological, socioeconomic, andhuman mobility context of COVID-19 can be considered as a complex adaptivesystem. So, these interventions (for example, lock-downs) could have manyand/or unexpected ramifications. This situation makes it difficult tounderstand the overall effect produced by any public policy measure and,therefore, to assess its real effectiveness and convenience. By using mobilephone data, socioeconomic data, and COVID-19 cases data recorded throughout thepandemic development, we aim to understand and explain [make sense of] theobserved heterogeneous regional patterns of contagion across time and space. Wewill also consider the causal effects produced by confinement policies bydeveloping data-based models to explore, simulate, and estimate these policies'effectiveness. We intend to develop a methodology to assess and improve publicpolicies' effectiveness associated with the fight against the pandemic,emphasizing its convenience, the precise time of its application, andextension. The contributions of this work can be used regardless of the region.The only likely impediment is the availability of the appropriate data.

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