Mathematical model optimized for prediction and health care planning for 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.