Evidence map›Paper›PMID 33738825›Full record

ArticleJournal of medical virology2021

The impact of awareness diffusion on the spread of COVID-19 based on a two-layer SEIR/V-UA epidemic model.

Xueke Zhao, Qingming Zhou, Anjing Wang, Fenping Zhu, Zeyang Meng, Chao Zuo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical virology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. 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

6 authors.

Xueke ZhaoSchool of Management Engineering and E-commerce, Zhejiang Gongshang University, Hangzhou, China.
Qingming ZhouSchool of Management Engineering and E-commerce, Zhejiang Gongshang University, Hangzhou, China.
Anjing WangSchool of Management Engineering and E-commerce, Zhejiang Gongshang University, Hangzhou, China.
Fenping ZhuSchool of Management Engineering and E-commerce, Zhejiang Gongshang University, Hangzhou, China.
Zeyang MengSchool of Management Engineering and E-commerce, Zhejiang Gongshang University, Hangzhou, China.
Chao ZuoSchool of Management Engineering and E-commerce, Zhejiang Gongshang University, Hangzhou, China.ORCID 0000-0002-5177-5813

Funding

General Research Project of Zhejiang Provincial Department of Education Y 202045064
6 · The paper itself

Abstract

In this paper, we propose a new susceptible-vaccinated-exposed-infected-recovered with unaware-aware (SEIR/V-UA) model to study the mutual effect between the epidemic spreading and information diffusion. We investigate the dynamic processes of the model with a Kinetic equation and derive the expression for epidemic stability by the eigenvalues of the Jacobian matrix. Then, we validate the model by the Monte Carlo method and numerical simulation on a two-layer scale-free network. With the outbreak of COVID-19, the spread of the epidemic in China prompted drastic measures for transmission containment. We examine the effects of these interventions based on modeling of the information-epidemic and the data of the COVID-19 epidemic case. The results further demonstrate that the epidemic spread can be affected by the effective transmission rate of awareness.

Indexed as

Health Knowledge, Attitudes, PracticeChinaCommunicable Disease ControlCOVID-19HumansModels, StatisticalPatient Education as TopicSARS-CoV-2awareness diffusionepidemic spreadingmultiplex networksSEIR/V-UA model

Identifiers

PMID33738825
PMCPMC8250399

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.