Evidence map›Paper›PMID 35086673›Full record

ArticleISA transactions2022

Qualitative and quantitative analysis of the COVID-19 pandemic by a two-side fractional-order compartmental model.

Weiyuan Ma, Yanting Zhao, Lihong Guo, YangQuan Chen

Abstract read
In one paragraph

Article in ISA transactions, 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.

  1. Article
  2. Article
  3. A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting.International journal of environmental research and public health · 2022
    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

4 authors.

Weiyuan MaSchool of Mathematics and Computer Science, Northwest Minzu University, Lanzhou 730030, China. Electronic address: jsmwy@xbmu.edu.cn.
Yanting ZhaoDepartment of Automation, University of Science and Technology of China, Hefei 230026, China.
Lihong GuoInstitute of Mathematics, Jilin University, Changchun 130015, China.
YangQuan ChenMechatronics, Embedded Systems and Automation Lab, University of California, Merced, CA 95343, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global efforts are focused on discussing effective measures for minimizing the impact of COVID-19 on global community. It is clear that the ongoing pandemic of this virus caused an immense threat to public health and economic development. Mathematical models with real data simulations are powerful tools that can identify key factors of pandemic and improve control or mitigation strategies. Compared with integer-order and left-hand side fractional models, two-side fractional models can better capture the state of pandemic spreading. In this paper, two-side fractional models are first proposed to qualitative and quantitative analysis of the COVID-19 pandemic. A basic framework are given for the prediction and analysis of infectious diseases by these types of models. By means of asymptotic stability analysis of disease-free and endemic equilibrium points, basic reproduction number R

Indexed as

COVID-19Basic Reproduction NumberEpidemiological ModelsHumansPandemicsSARS-CoV-2AnalysisCOVID-19Fractional orderGeneralized SEIR modelTwo-side

Identifiers

PMID35086673
PMCPMC8753533

What OpenQuestion holds

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Registered trials

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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.