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.

Introduction: The COVID-19 pandemic has triggered a better preparation of primary care, hospitalization, and emergency health services. The current investigation of COVID-19 dynamics was carried at the Hospital Fundació Sant Joan de Deu de Martorell (FHSJDM). This research aims to analyse the COVID-19 time series in FHSJDM. Methods: The time series of COVID-19 were analized for 2020-2021. To measure seasonality, the Dicky-Fuller test was obtained. The analysis of the results was performed in R-Studio software. Results: Three peaks of cases are observed for January, April, and July 2021. These peaks of hospital cases are correlated with the new cases in the municipality and also with the new cases in Catalonia. The result of the seasonality test has a p-value >0.05, and thus it is accepted that the series is not seasonal for the registry of hospital cases. Discussion: The study of COVID-19 dynamics is relevant to preparing health services. Each peak observed in the 2021 period affected the health services in the hospital FHSJDM, having an increase for new cases, hospitalized patients and total cases for COVID-19.

Background: SARS-CoV-2 is a new type of coronavirus that causes COVID-19. It is affecting the entire planet. Despite the widespread use of ecologic analysis in epidemiologic research and health planning, health scientists and practitioners have given little attention to the methodological aspects of this approach. The study of risk factors linked to the COVID-19 pandemic is one of the most current and exciting topics for epidemiologists. These risks in many cases are unknown. This research covers the study of risk factors in the case of COVID-19 and proposes the use of an ecologic method known to epidemiologists in the case of aggregated data. The present study aims to compute a model that allows to easily calculate the risk of infection in different types of populations, using aggregated data to approximate the individual risk of COVID-19 transmission by a person. Methods: The case of Catalonia, in Spain, is presented as an example, as it is one of the areas where the incidence of the virus among the population is being higher. The proposed method is known as an ecological study and is based on the statistical regression model between the incidence (or variable that represents it) and the risk factors but using aggregated data and obtaining a risk ratio (RR). Results: The results obtained have made it possible to find the risk of contracting COVID-19 concerning risk factors for high family income (RR=1.157491), more mobility (RR=1.065475), and high density of population (1.00002). Conclusions: This method could be used to design an app that predicts how the risk will evolve and calculate the risk of contagion in one area or another to take the proper action. The calculated RR can help us to understand how the variables become risks or protective factors at an ecological level (understanding aggregate data).