Mathematical model optimized for prediction and health care planning for COVID-19

J M Garrido; D Martínez-Rodríguez; F Rodríguez-Serrano; J M Pérez-Villares; A Ferreiro-Marzal; M M Jiménez-Quintana; Grupo de Estudio COVID 19 Granada; R J Villanueva

doi:10.1016/j.medin.2021.02.014

Mathematical model optimized for prediction and health care planning for COVID-19

Med Intensiva (Engl Ed). 2021 Mar 6;46(5):248-258. doi: 10.1016/j.medin.2021.02.014. Online ahead of print.

[Article in English, Spanish]

Authors

J M Garrido¹, D Martínez-Rodríguez², F Rodríguez-Serrano³, J M Pérez-Villares⁴, A Ferreiro-Marzal⁵, M M Jiménez-Quintana⁴; Grupo de Estudio COVID 19 Granada; R J Villanueva²

Affiliations

¹ Instituto 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. Electronic address: josemgarrido@ugr.es.
² Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, Valencia, España.
³ Instituto de Investigación Biosanitaria ibs, GRANADA, Granada, España; Instituto de Biopatología y Medicina Regenerativa (IBIMER), Universidad de Granada, Granada, España.
⁴ Servicio de Medicina Intensiva, Hospital Universitario Virgen de las Nieves, Granada, España.
⁵ Servicio de Cirugía Cardiovascular, Hospital Virgen de las Nieves, Granada, España.

Abstract

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.

Keywords: COVID-19; Epidemiological prediction; Hospitalización; Hospitalization; ICU; Mathematical model; Modelo matemático; Pandemia; Pandemic; Predicción epidemiológica; Prevalence; Prevalencia; SARS-CoV-2; UCI.