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针对新冠病毒病预测和医疗保健规划进行优化的数学模型。

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

作者信息

Garrido J M, Martínez-Rodríguez D, Rodríguez-Serrano F, Pérez-Villares J M, Ferreiro-Marzal A, Jiménez-Quintana M M, Villanueva R J

机构信息

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.

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

出版信息

Med Intensiva (Engl Ed). 2021 Mar 6;46(5):248-58. doi: 10.1016/j.medin.2021.02.014.

DOI:10.1016/j.medin.2021.02.014
PMID:33926752
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7936565/
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

新冠疫情可能导致医院和重症监护病房（ICU）服务崩溃，并影响了非新冠患者的护理计划。目的是开发一种数学模型，以优化与新冠患者住院和入住ICU需求相关的预测。

设计

前瞻性研究。

地点

西班牙格拉纳达省。

研究对象

2020年3月15日至9月22日期间因新冠住院、入住ICU、康复及死亡的新冠患者。

研究变量

感染严重急性呼吸综合征冠状病毒2（SARS-CoV-2）并因新冠住院或入住ICU的患者数量。

结果

利用医院报告的数据开发了一个数学模型，该模型反映了与新冠相关的不同利益群体之间的人口流动情况。该工具能够根据社会卫生限制措施分析不同情景，并预测感染、住院和入住ICU的人数。

结论

该数学模型能够提前充分预测新冠疫情的发展，从而预测患病率高峰以及医院和ICU护理需求高峰，还能预测可以加强对非新冠患者护理的时期的出现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/f068676a59fa/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/bb7f773841f2/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/e2b35260d77e/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/159a574b777f/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/2f86193c52a7/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/f068676a59fa/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/bb7f773841f2/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/e2b35260d77e/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/159a574b777f/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/2f86193c52a7/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8082/7936565/f068676a59fa/gr5_lrg.jpg

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