International Journal of Mathematics Trends and Technology
Volume 66 | Issue 11 | Year 2020 | Article Id. IJMTT-V66I11P504 | DOI : https://doi.org/10.14445/22315373/IJMTT-V66I11P504
In an application of the mathematical theory of statistics, predictive regression modelling can be used to determine if there is a trend to predict the response variable of social distancing in terms of multiple predictor input “predictor” variables. In this study the social distancing is measured as the percentage reduction in average mobility by GPS records, and the mathematical results obtained are interpreted to determine what factors drive that response. This study was done on county level data from the state of Florida during the COVID-19 pandemic, and it is found that the most deterministic predictors are county population density along with median income.
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[5] Miami Dade County COVID project data site https://rwilli5.github.io/MiamiCovidProject/Trajectory/
[6] Wikipedia list of Florida counties https://en.wikipedia.org/wiki/List_of_counties_in_Florida
[7] Florida Survey of Income https://www.countyhealthrankings.org/app/florida/2020/measure/factors/63/data
[8] Florida University Study of Demographics http://edr.state.fl.us/Content/population-demographics/data/PopulationEstimates2019.pdf
Timothy A. Smith, Albert J. Boquet, Matthew Chin, "A Statistical Learning Regression Model utilized to determine predictive factors of social distancing during COVID-19 pandemic," International Journal of Mathematics Trends and Technology (IJMTT), vol. 66, no. 11, pp. 60-66, 2020. Crossref, https://doi.org/10.14445/22315373/IJMTT-V66I11P504
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