Evidence map›Paper›PMID 36891356›Full record

ArticleChaos, solitons, and fractals2023

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic.

Xueyu Meng, Jianhong Lin, Yufei Fan, Fujuan Gao, Enrico Maria Fenoaltea, Zhiqiang Cai, Shubin Si

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

Article in Chaos, solitons, and fractals, 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
5.1field-weighted citation impact, top 4% 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, 24 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.

Xueyu MengDepartment of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
Jianhong LinDepartment of Physics, University of Fribourg, Fribourg 1700, Switzerland.
Yufei FanDepartment of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
Fujuan GaoDepartment of Physics, University of Fribourg, Fribourg 1700, Switzerland.
Enrico Maria FenoalteaDepartment of Physics, University of Fribourg, Fribourg 1700, Switzerland.
Zhiqiang CaiDepartment of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
Shubin SiDepartment of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China.
University of Fribourg · CHMinistry of Industry and Information Technology · CNNorthwestern Polytechnical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.

Indexed as

Compartment modelCOVID-19Epidemic spreadingEvolutionary game theoryVaccination behavior

Identifiers

PMID36891356
PMCPMC9977628
OpenAlexW4322772704

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.