Evidence map›Paper›PMID 39500981›Full record

ArticleScientific reports2024

Analysis of stochastic epidemic model with awareness decay and heterogeneous individuals on multi-weighted networks.

Xin Yi, Guirong Liu

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Xin YiSchool of Mathematics and Statistics, Taiyuan Normal University, Jinzhong, 030619, People's Republic of China.
Guirong LiuSchool of Mathematical Sciences, Shanxi University, Taiyuan, 030006, People's Republic of China. lgr5791@sxu.edu.cn.

Funding

National Natural Science Foundation of China 11971279Shanxi Provincial Key Research and Development Project 202202020101010
6 · The paper itself

Abstract

In the current study, we present a stochastic SAIS (unaware susceptible-aware susceptible-infectious-unaware susceptible) epidemic dynamic model on complex networks with multi-weights. The disease dynamic is influenced by random perturbations to the force of the infection rates, as well as awareness rates. To analyze the problem of extinction, we discuss both the stochastic asymptotic stability in the large and almost surely exponential stability of the trivial solution. Then, we get some sufficient conditions, which guarantee the stochastic persistence of infectious disease. Based on the Erdös-Réyni random graph, the numerical simulations are given. These not only validate our conclusions but also obtain else significative results. Both theoretical results and numerical simulations further reflect that improvement of risk awareness and reduction of decay in awareness are highly effective in preventing disease spread. And then, environmental noises play a significant role in disease transmission.

Indexed as

Communicable DiseasesEpidemicsStochastic ProcessesAwarenessComputer SimulationDisease SusceptibilityHumansModels, TheoreticalExtinctionMulti-weighted networksStochastic persistenceStochastic SAIS model

Identifiers

PMID39500981
PMCPMC11538551

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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.