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Vol. 46. Issue 5.
Pages 248-258 (May 2022)
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Vol. 46. Issue 5.
Pages 248-258 (May 2022)
Original article
Mathematical model optimized for prediction and health care planning for COVID-19
Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19
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J.M. Garridoa,b,c,
Corresponding author
josemgarrido@ugr.es

Corresponding author.
, D. Martínez-Rodríguezd, F. Rodríguez-Serranoa,b, J.M. Pérez-Villarese, A. Ferreiro-Marzalc, M.M. Jiménez-Quintanae, Study Group COVID 19 Granada , R.J. Villanuevad
a Instituto de Investigación Biosanitaria ibs, GRANADA, Granada, Spain
b Instituto de Biopatología y Medicina Regenerativa (IBIMER), Universidad de Granada, Granada, Spain
c Servicio de Cirugía Cardiovascular, Hospital Virgen de las Nieves, Granada, Spain
d Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, Valencia, Spain
e Servicio de Medicina Intensiva, Hospital Universitario Virgen de las Nieves, Granada, Spain
Article information
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Tables (2)
Table 1. Population groups in relation to SARS-CoV-2 infection and the evolution of the COVID-19 pandemic, and equations predicting the quantification of each group in each moment in time.
Table 2. Number of patients infected with SARS-CoV-2, hospitalized and admitted to the ICU predicted by the mathematical model on 10 November 2020 for three scenarios that differ in the calendar and duration of the application of restriction measures for the province of Granada (Spain).
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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
SARS-CoV-2
Mathematical model
Hospitalization
ICU
Pandemic
Prevalence
Epidemiological prediction
Resumen
Objetivo

La pandemia de la COVID-19 ha supuesto una amenaza de colapso de los servicios hospitalarios y de unidades de cuidado intensivo (UCI), así como una reducción de la dinámica asistencial de pacientes afectados por otras patologías. El objetivo fue desarrollar un modelo matemático diseñado para optimizar las predicciones relacionadas con las necesidades de hospitalización e ingresos en UCI por la COVID-19.

Diseño

Estudio prospectivo.

Ámbito

Provincia de Granada (España).

Pacientes

Pacientes de COVID-19 hospitalizados, ingresados en UCI, recuperados y fallecidos desde el 15 de marzo hasta el 22 de septiembre del 2020.

Intervenciones

Desarrollo de un modelo matemático tipo susceptible, expuesto, infectado y recuperado (SEIR) capaz de predecir la evolución de la pandemia, considerando las medidas de salud pública establecidas.

Variables de interés

Número de pacientes infectados por SARS-CoV-2, hospitalizados e ingresados en UCI por la COVID-19.

Resultados

A partir de los datos registrados, hemos podido desarrollar un modelo matemático que refleja el flujo de la población entre los diferentes grupos de interés en relación con la COVID-19. Esta herramienta permite analizar diferentes escenarios basados en medidas de restricción socio-sanitarias y pronosticar el número de infectados, hospitalizados e ingresados en UCI.

Conclusiones

El modelo matemático es capaz de proporcionar predicciones sobre la evolución de la COVID-19 con suficiente antelación como para poder conjugar los picos de prevalencia y de necesidades de asistencia hospitalaria y de UCI, con la aparición de ventanas temporales que posibiliten la atención de enfermos no-COVID.

Palabras clave:
COVID-19
SARS-CoV-2
Modelo matemático
Hospitalización
UCI
Pandemia
Prevalencia
Predicción epidemiológica

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