![]() ![]() Therefore, it is necessary to implement policies that consider these variables. Health policies had an effect on slowing the pandemic’s propagation, but population density and mobility played a fundamental role. The policy index (coefficient 0.60, p < 0.01) and the income per capita (coefficient 3.36, p < 0.01) had a positive effect on doubling time by contrast, the population density (coefficient −0.012, p < 0.05), the mobility in parks (coefficient −1.10, p < 0.01) and the residential mobility (coefficient −4.14, p < 0.01) had a negative effect. Delay in the issuance of policies was associated with accelerated propagation. States with larger population sizes issued a larger number of policies. ![]() A panel data model was applied to measure the effect of these variables on doubling time. Additionally, variables such as population size and density, poverty and mobility were included. Policies issued by each of the 32 Mexican states during each week of this period were classified according to the University of Oxford Coronavirus Government Response Tracker (OxCGRT), and the doubling time of COVID-19 cases was calculated. A retrospective longitudinal study was carried out across March–August, 2020. ![]() The aim of the present investigation was to determine the impact of policies and several sociodemographic factors on the COVID-19 doubling time in Mexico. The doubling time is the best indicator of the course of the current COVID-19 pandemic. ![]()
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