Evidence map›Paper›PMID 33851729›Full record

ArticleJournal of medical virology2021

Network dynamic model of epidemic transmission introducing a heterogeneous control factor.

Huaxiong Sheng, Lin Wu, Tingting Wu, Bo Peng

Abstract read
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 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

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

Huaxiong ShengGraduate School of National Defense University, Beijing, China.ORCID http://orcid.org/0000-0003-3680-1064
Lin WuJoint Operation College of National Defense University, Beijing, China.ORCID http://orcid.org/0000-0002-6433-9201
Tingting WuGraduate School of National Defense University, Beijing, China.
Bo PengJoint Operation College of National Defense University, Beijing, China.ORCID http://orcid.org/0000-0002-5321-1771

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 epidemic is not only a medical issue but also a sophisticated social problem. We propose a network dynamics model of epidemic transmission introducing a heterogeneous control factor. The proposed model applied the classical susceptible- exposed-infectious-recovered model to the network based on effective distance and was modified by introducing a heterogeneous control factor with temporal and spatial characteristics. International aviation data were approximately used to estimate the flux fraction matrix, and the effective distance was calculated. Through parameter estimation and simulation, the theoretical values of the modified model fit well with practical values. By adjusting the parameters and observing the change of the results, we found that the modified model is more in line with the actual needs and has higher credibility in the comprehensive analysis. The assessment shows that the number of confirmed cases worldwide will reach about 20 million optimistically. In severe cases, the peak value will exceed 80 million, and the late stage of the epidemic shows a long tail shape, lasting more than one and a half years. The effective way to control the global epidemic is to strengthen international cooperation and to impose international travel restrictions and other measures.

Indexed as

Communicable Disease ControlComputer SimulationCOVID-19HumansPhysical DistancingPrimary PreventionQuarantineSARS-CoV-2COVID-19heterogeneousmodelingnetwork dynamics

Identifiers

PMID33851729
PMCPMC8250401

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.