In response to the COVID-19 pandemic, most governments around the world implemented some kind of social distancing policy in an attempt to block the spreading of the virus within a territory. In Brazil, this mitigation strategy was first implemented in March 2020 and mainly monitored by social isolation indicators built from mobile geolocation data. While it is well known that social isolation has been playing a crucial role in epidemic control, the precise connections between mobility data indicators and epidemic dynamic parameters have a complex interdependence. In this work, we investigate this dependence for several Brazilian cities, looking also at socioeconomic and demographic factors that influence it. As expected, the increase in the social isolation indicator was shown to be related to the decrease in the speed of transmission of the disease, but the relation was shown to depend on the urban hierarchy level of the city, the human development index and also the epidemic curve stage. Moreover, a high social isolation at the beginning of the epidemic relates to a strong positive impact on flattening the epidemic curve, while less efficacy of this mitigation strategy was observed when it has been implemented later. Mobility data plays an important role in epidemiological modeling and decision-making, however, we discuss in this work how a direct relationship between social isolation data and COVID-19 data is hard to be established. Understanding this interplay is a key factor to better modeling, for which we hope this study contributes.
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