ArticleMedicina intensiva2022
Mathematical model optimized for prediction and health care planning for COVID-19.
Article in Medicina intensiva, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Mathematical Modeling of Obstetric Variables: Influence of COVID-19, Periodontal Disease and Dental Care During Pregnancy.Biomedicines · 2025Article
- A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo.Heliyon · 2024Article
- Analysis of COVID-19 mathematical model for predicting the impact of control measures in Rwanda.Informatics in medicine unlocked · 2023Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe 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.
designProspective study.
settingProvince 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.
resultsThe 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.
conclusionsThe 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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Registered trials
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