1 Department of Genetics, Section of Statistics, Microbiology, and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Spain.
2 Department of Clinical Foundations, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain.
3 Department of Administration and Political Science, Faculty of Law and Social Sciences, University of Talca, Chile.
Research Article
World Journal of Advanced Research and Reviews, 2023, 17(01), 531-536
Article DOI: 10.30574/wjarr.2023.17.1.0098
Received on 08 December 2022; revised on 16 January 2023; accepted on 19 January 2023
Background: The COVID-19 pandemic has had a global impact. Knowing the variables that affect the increase in infection is crucial for public health decision-making. Mobility and socio-demographic conditions of the population are important factors in the transmission of the SARS-CoV-2. The objective of this study is to analyze the relationship between people mobility, social determinants of health and COVID-19 cases using a Random Forest (RF) method.
Methods: The COVID-19 cases were analyzed in the Maule Region, Chile. Spearman rank was performed to analyze the total mobility index for each municipality. RF regression was used to create a model between COVID-19 infections, mobility index and sociodemographic variables. P-value <0.05 was considered statistically significant.
Results: Total mobility was highly correlated with new COVID-19 cases, adjusted for total population, in each municipality (ρ: 0.52-0.92). An upward trend is observed for mobility and COVID-19 cases for the 30 municipalities analyzed. For the RF model, COVID-19 active cases, total mobility, and external mobility are obtained as VIM. The most relevant demographic variables were overcrowding, density and area of municipality. The R-Squared was 0.68 for the performed RF model.
Conclusions: Artificial Intelligence methodologies are increasingly used for their excellent performance. RF Regression offers a clear solution for the design of predictor variables on the number of new cases per week. Mobility is a powerful predictor variable for the number of COVID-19 new cases.
Random Forest; Mobility; Pandemics; COVID-19
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Nicolas Ayala, Antonio Monleon-Getino, Jaume Canela-Soler and Tomas Chadwick-Lobos. Random forest using smartphone GPS in first wave of COVID-19 in the Maule region, Chile. World Journal of Advanced Research and Reviews, 2023, 17(1), 531-536. Article DOI: https://doi.org/10.30574/wjarr.2023.17.1.0098