DYNAMIC SIMULATION AND POLICY OPTIMIZATION OF EPIDEMIC SPREAD IN HETEROGENEOUS NETWORKS A Study Based on the Susceptible-Infectious-Recovered-Susceptible Model.
Saved in:
| Title: | DYNAMIC SIMULATION AND POLICY OPTIMIZATION OF EPIDEMIC SPREAD IN HETEROGENEOUS NETWORKS A Study Based on the Susceptible-Infectious-Recovered-Susceptible Model. |
|---|---|
| Authors: | LUO, Zhibin1, LI, Haiyan2 13614810768@163.com, LI, Haibin1 |
| Source: | Thermal Science. 2026, Vol. 30 Issue 2A, p1039-1045. 7p. |
| Subjects: | Epidemiological models, Scale-free network (Statistical physics), Health planning, Health policy, COVID-19 pandemic, Epidemics |
| Abstract: | This study investigates the dynamic processes of epidemic spread and policy optimization in heterogeneous Barabasi-Albert networks using the susceptible-infectious-recovered-susceptible model. By systematically adjusting key parameters, including the number of inter-subnetwork connections, the number of subnetworks, and node connection strategies, we conducted a comprehensive analysis of the impact of these factors on epidemic transmission. This analysis revealed the critical role of network structure in disease spread. The findings indicate that augmenting inter-subnetwork connections and node connections expedites the propagation of the epidemic, culminating in elevated infection peaks but diminished overall epidemic durations. The model validation is further substantiated by the use of real-world data from the outbreak of the novel coronavirus disease in Wuhan, China, with the simulation results demonstrating a close alignment with the observed trends. This finding serves to substantiate the model's efficacy. Studies on policy optimization have indicated that the premature relaxation of control measures can result in elevated infection peaks. Conversely, the easing of measures at opportune times can facilitate more effective epidemic control. This research establishes a theoretical framework for public health decision-making, particularly in terms of balancing epidemic control with socio-economic recovery. [ABSTRACT FROM AUTHOR] |
| Copyright of Thermal Science is the property of Society of Thermal Engineers of Serbia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193183537 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: DYNAMIC SIMULATION AND POLICY OPTIMIZATION OF EPIDEMIC SPREAD IN HETEROGENEOUS NETWORKS A Study Based on the Susceptible-Infectious-Recovered-Susceptible Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22LUO%2C+Zhibin%22">LUO, Zhibin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22LI%2C+Haiyan%22">LI, Haiyan</searchLink><relatesTo>2</relatesTo><i> 13614810768@163.com</i><br /><searchLink fieldCode="AR" term="%22LI%2C+Haibin%22">LI, Haibin</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Thermal+Science%22">Thermal Science</searchLink>. 2026, Vol. 30 Issue 2A, p1039-1045. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Epidemiological+models%22">Epidemiological models</searchLink><br /><searchLink fieldCode="DE" term="%22Scale-free+network+%28Statistical+physics%29%22">Scale-free network (Statistical physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Health+planning%22">Health planning</searchLink><br /><searchLink fieldCode="DE" term="%22Health+policy%22">Health policy</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br /><searchLink fieldCode="DE" term="%22Epidemics%22">Epidemics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study investigates the dynamic processes of epidemic spread and policy optimization in heterogeneous Barabasi-Albert networks using the susceptible-infectious-recovered-susceptible model. By systematically adjusting key parameters, including the number of inter-subnetwork connections, the number of subnetworks, and node connection strategies, we conducted a comprehensive analysis of the impact of these factors on epidemic transmission. This analysis revealed the critical role of network structure in disease spread. The findings indicate that augmenting inter-subnetwork connections and node connections expedites the propagation of the epidemic, culminating in elevated infection peaks but diminished overall epidemic durations. The model validation is further substantiated by the use of real-world data from the outbreak of the novel coronavirus disease in Wuhan, China, with the simulation results demonstrating a close alignment with the observed trends. This finding serves to substantiate the model's efficacy. Studies on policy optimization have indicated that the premature relaxation of control measures can result in elevated infection peaks. Conversely, the easing of measures at opportune times can facilitate more effective epidemic control. This research establishes a theoretical framework for public health decision-making, particularly in terms of balancing epidemic control with socio-economic recovery. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Thermal Science is the property of Society of Thermal Engineers of Serbia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193183537 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/TSCI2602039L Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 1039 Subjects: – SubjectFull: Epidemiological models Type: general – SubjectFull: Scale-free network (Statistical physics) Type: general – SubjectFull: Health planning Type: general – SubjectFull: Health policy Type: general – SubjectFull: COVID-19 pandemic Type: general – SubjectFull: Epidemics Type: general Titles: – TitleFull: DYNAMIC SIMULATION AND POLICY OPTIMIZATION OF EPIDEMIC SPREAD IN HETEROGENEOUS NETWORKS A Study Based on the Susceptible-Infectious-Recovered-Susceptible Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: LUO, Zhibin – PersonEntity: Name: NameFull: LI, Haiyan – PersonEntity: Name: NameFull: LI, Haibin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 03549836 Numbering: – Type: volume Value: 30 – Type: issue Value: 2A Titles: – TitleFull: Thermal Science Type: main |
| ResultId | 1 |
