Highlights Developed models to forecast bed occupancy for up to 14 days and monitored errors for 365 days. Telemedicine calls from COVID-19 patients correlated withthe number of patients hospitalized in the next 8 days. ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as […]

ABSTRACT Objective To assess Google Trends accuracy for epidemiological surveillance of dengue and yellow fever, and to compare the incidence of these diseases with the popularity of its terms in the state of São Paulo. Methods Retrospective cohort. Google Trends survey results were compared to the actual incidence of diseases, obtained from Centro de Vigilância Epidemiológica “Prof. Alexandre Vranjac”, in São Paulo, Brazil, in periods between 2017 and 2019. The correlation was calculated by Pearson’s coefficient and cross-correlation function. The […]

ABSTRACT Objective To propose a predictive model for the length of stay risk among children admitted to a pediatric intensive care unit based on demographic and clinical characteristics upon admission. Methods This was a retrospective cohort study conducted at a private and general hospital located in the municipality of Sao Paulo, Brazil. We used internal validation procedures and obtained an area under ROC curve for the to build of the predictive model. Results The mean hospital stay was 2 days. […]