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Mathematical model optimized for prediction and health care planning for COVID-19
J M Garrido1, D Martínez-Rodríguez2, F Rodríguez-Serrano3
1Instituto de Investigación Biosanitaria ibs, GRANADA, Granada, España; Instituto de Biopatología y Medicina Regenerativa (IBIMER), Universidad de Granada, Granada, España; Servicio de Cirugía Cardiovascular, Hospital Virgen de las Nieves, Granada, España.
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.
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