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针对 COVID-19 进行预测和医疗保健规划的优化数学模型。

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

机构信息

Instituto de Investigación Biosanitaria ibs, GRANADA, Granada, Spain; Instituto de Biopatología y Medicina Regenerativa (IBIMER), Universidad de Granada, Granada, Spain; Servicio de Cirugía Cardiovascular, Hospital Virgen de las Nieves, Granada, Spain.

Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, Valencia, Spain.

出版信息

Med Intensiva (Engl Ed). 2022 May;46(5):248-258. doi: 10.1016/j.medine.2022.02.020. Epub 2022 Feb 28.

DOI:10.1016/j.medine.2022.02.020
PMID:35256322
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8882409/
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.

摘要

目的

新冠疫情大流行威胁到医院和 ICU 服务的崩溃，并影响了非新冠患者的护理计划。本研究旨在开发一种数学模型，旨在优化与 COVID-19 患者住院和 ICU 入院需求相关的预测。

设计

前瞻性研究。

地点

西班牙格拉纳达省。

人群

2020 年 3 月 15 日至 9 月 22 日期间，因 COVID-19 住院、转入 ICU、康复和死亡的患者。

研究变量

感染 SARS-CoV-2 的患者人数以及因 COVID-19 住院或转入 ICU 的患者人数。

结果

使用医院报告的数据开发了一个数学模型，该模型反映了与 COVID-19 相关的不同利益群体之间的人群流动。该工具可根据社会卫生限制措施分析不同的场景，并预测感染、住院和转入 ICU 的人数。

结论

该数学模型能够提前足够长的时间提供关于 COVID-19 演变的预测，以预测流行高峰期和医院及 ICU 护理需求，以及出现可以加强非 COVID 患者护理的时期。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/ee1509fbf985/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/aac5f9eb4b19/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/beb952a9a7ce/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/8a706ce184a8/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/cca892a884b1/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/ee1509fbf985/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/aac5f9eb4b19/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/beb952a9a7ce/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/8a706ce184a8/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/cca892a884b1/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef00/8882409/ee1509fbf985/gr5_lrg.jpg

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