Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19

OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.