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

TL;DR

This study develops a mathematical model to predict COVID-19 hospitalizations and ICU needs, aiding healthcare planning and resource allocation during the pandemic.

Contribution

A novel mathematical model that forecasts COVID-19 hospital and ICU demands, enabling proactive healthcare management and planning.

Findings

The model accurately predicts COVID-19 hospitalizations and ICU admissions.

It allows scenario analysis based on socio-health restrictions.

Predictions can be made several months in advance.

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. Consecutive 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 has allowed us to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU until May 2021. 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.