Evidence map›Paper›PMID 40236658›Full record

ArticleRisk management and healthcare policy2025

Subject Modeling-Based Analysis of the Evolution and Intervention Strategies of Major Emerging Infectious Disease Events.

Haixiang Guo, Tiantian Zhao, Yuzhe Zou, Beijia Zhang, Yuyan Cheng

Abstract read
In one paragraph

Article in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

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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

5 authors.

Haixiang GuoSchool of Economics and Management, China University of Geosciences, Wuhan, People's Republic of China.
Tiantian ZhaoSchool of Economics and Management, China University of Geosciences, Wuhan, People's Republic of China.ORCID 0009-0005-7061-5646
Yuzhe ZouSchool of Economics and Management, China University of Geosciences, Wuhan, People's Republic of China.ORCID 0000-0002-3770-4197
Beijia ZhangSchool of Economics and Management, China University of Geosciences, Wuhan, People's Republic of China.
Yuyan ChengSchool of Economics and Management, China University of Geosciences, Wuhan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Due to the popularity of the Internet and the extensive use of new media, after the occurrence of infectious diseases, the spread of social media information greatly affects the group's opinion and cognition and even the health behaviors they take, thus affecting the spread of infectious diseases. Therefore, this paper studies the event evolution from multiple dimensions. Methods: To address this gap, we developed a three-layer model framework of major infectious disease event evolution based on subject modeling. This framework integrates three key factors-health transmission, perspective interaction, and risk perception-to analyze group perspective evolution, behavioral change, and virus transmission processes. The model's effectiveness was evaluated through simulation and sensitivity analysis. In addition, we conducted an empirical analysis by constructing a social media health transmission effect index system to identify the critical factors affecting health transmission. Results: Simulation results reveal that among the three factors, health transmission has the most significant impact on the evolution of group perspectives during infectious disease events. Moreover, the dynamics of public viewpoint evolution influence individual decisions regarding the adoption of non-pharmacological interventions, which are shown to effectively reduce both the transmission rate of the virus and the peak number of infections. Conclusion: The findings of this study enhance our understanding of the complex mechanisms and evolutionary pathways in infectious disease events. By integrating multiple dimensions of event evolution, the proposed model offers valuable insights for the design of effective countermeasures and strategies in emergency management and response to infectious disease outbreaks.

Indexed as

behavior changeinfectious disease eventssimulationsubject modelingviewpoint evolution

Identifiers

PMID40236658
PMCPMC11998951

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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.