Evidence map›Paper›PMID 35463288›Full record

ArticleComputational intelligence and neuroscience2022

Optimization of Site Selection for Emergency Medical Facilities considering the SEIR Model.

Jingkuang Liu, Liying Cao, Dongyu Zhang, Zibo Chen, Xiaotong Lian, Ying Li, Yingyi Zhang

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jingkuang LiuSchool of Management, Guangzhou University, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.ORCID https://orcid.org/0000-0001-6542-470X
Liying CaoSchool of Management, Guangzhou University, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Dongyu ZhangSchool of Management, Guangzhou University, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Zibo ChenSchool of Mathematics and Information Science, Guangzhou University, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Xiaotong LianSchool of Management, Guangzhou University, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Ying LiSchool of Management, Guangzhou University, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Yingyi ZhangSchool of Management, Guangzhou University, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the outbreak of COVID-19, the rapid construction and operation of Wuhan Vulcan Mountain Hospital and Raytheon Hospital have attracted positive responses from local and international observers. At the same time, it has also highlighted the urgency for the construction of emergency medical facilities for public health emergencies. Before construction, the practical location of medical facilities is the basis for improving the city's emergency management ability. Based on the classic susceptible, exposed, infected, and recovered (SEIR) epidemic model and epidemic data in Guangzhou, we established a multi-stage time-delay SEIR epidemic model that is suitable for epidemic research in Guangzhou. According to the results of the model, the five areas with the highest number of infected patients were identified, which included Baiyun District, Panyu District, Haizhu District, Tianhe District, and Zengcheng District. We then centralized infected individuals at five demand points. Based on the distribution of these points and by combining the characteristics of the emergency medical facilities, we built and solved the set covering location decision model, and considered the economy, society, and environment as the starting points to optimize the site location. Finally, based on simulations, we concluded that appropriate site selection can increase the time required to reach the maximum number of patients and reduce the proportion of infected and exposed people by 11.3% and 1.11%, respectively. This is indicative of the effectiveness of the site selection model and the rational selection of facility points in this study. It solves the optimization problem of the location decision of emergency medical facilities for public health emergencies in China, and also provides some valuable references for site selection decisions of emergency medical facilities in other areas.

Indexed as

COVID-19ChinaCitiesEmergenciesHumansPublic Health

Identifiers

PMID35463288
PMCPMC9020410

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.