Evidence map›Paper›PMID 36567189›Full record

ArticleISA transactions2023

The effect mitigation measures for COVID-19 by a fractional-order SEIHRDP model with individuals migration.

Zhenzhen Lu, YangQuan Chen, Yongguang Yu, Guojian Ren, Conghui Xu, Weiyuan Ma, Xiangyun Meng

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ISA transactions, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
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

1 citing paper in PubMed, 10 citations in OpenAlex.

  1. Article
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 at 3 institutions in 2 countries.

Zhenzhen LuDepartment of Mathematics, Beijing Jiaotong University, Beijing, 100044, PR China.
YangQuan ChenMechatronics, Embedded Systems and Automation Lab, University of California, Merced, CA 95343, USA.
Yongguang YuDepartment of Mathematics, Beijing Jiaotong University, Beijing, 100044, PR China. Electronic address: ygyu@bjtu.edu.cn.
Guojian RenDepartment of Mathematics, Beijing Jiaotong University, Beijing, 100044, PR China.
Conghui XuDepartment of Mathematics, Beijing Jiaotong University, Beijing, 100044, PR China.
Weiyuan MaSchool of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730000, PR China.
Xiangyun MengDepartment of Mathematics, Beijing Jiaotong University, Beijing, 100044, PR China.
Beijing Jiaotong University · CNMinzu University of China · CNUniversity of California, Merced · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, the generalized SEIHRDP (susceptible-exposed-infective-hospitalized-recovered-death-insusceptible) fractional-order epidemic model is established with individual migration. Firstly, the global properties of the proposed system are studied. Particularly, the sensitivity of parameters to the basic reproduction number are analyzed both theoretically and numerically. Secondly, according to the real data in India and Brazil, it can all be concluded that the bilinear incidence rate has a better description of COVID-19 transmission. Meanwhile, multi-peak situation is considered in China, and it is shown that the proposed system can better predict the next peak. Finally, taking individual migration between Los Angeles and New York as an example, the spread of COVID-19 between cities can be effectively controlled by limiting individual movement, enhancing nucleic acid testing and reducing individual contact.

Indexed as

COVID-19EpidemicsBasic Reproduction NumberCitiesHumansSARS-CoV-2Fractional-order epidemic modelIndividual migrationPeak predictionSensitivity

Identifiers

PMID36567189
PMCPMC9748852
OpenAlexW4311485126

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.