Traffic-driven SI epidemic spreading on scale-free networks

Zhang, Yongqiang; Li, Shuang; Zhang, Junfeng; Ma, Jinlong; An, Haigang

doi:10.1142/s012918312250111x

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…Finally, traffic congestion spreading has also been investigated as a contagion process (e.g., [49], [50], [51], [52], [10]). In this approach, the road network is abstracted and represented as a graph consisting of a collection of nodes (road junctions/intersections in our case) and links (road segments).…”

Section: Related Workmentioning

confidence: 99%

Modeling Traffic Congestion Spreading Using a Topology-Based SIR Epidemic Model

Kozhabek,

Chai,

Zheng

2024

IEEE Access

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The continuous urbanisation and increase in vehicle ownership have increasingly exacerbated traffic congestion problems. In this paper, we advocate the use of epidemic theory to model the spreading of traffic congestion in urban cities. Specifically, we use the Susceptible-Infected-Recovered (SIR) model but propose to explicitly consider the road network structure in the model to understand the contagion process of road congestion. This departs from the classical SIR model where homogeneous mixing based on the law of mass action is assumed. For this purpose, we adopt the N-intertwined modeling framework for the SIR model based on continuous-time Markov chain analysis. In our evaluation, we used two real-world traffic datasets collected in California and Los Angeles. We compare our results against both classical and average-degreebased SIR models. Our results show better agreement between the model and actual congestion conditions and shed light on how congestion propagates across a road network. We see the potential application of insights gained from this work on the development of traffic congestion mitigation strategies.INDEX TERMS Congestion spread modeling, epidemics, SIR model, topology,traffic congestion, urban road networks.

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Section: Related Workmentioning

confidence: 99%

Modeling Traffic Congestion Spreading Using a Topology-Based SIR Epidemic Model

Kozhabek,

Chai,

Zheng

2024

IEEE Access

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“…In the construction of infectious disease models, the classical propagation models susceptible-infectious (SI), susceptible-infectious-recovered (SIR), and susceptible exposed infectious removed (SEIR) are commonly used. For example, Zhang et al (2022) used the SI model to investigate the relationship between link closure strategies and traffic-driven epidemic propagation. They all used the SIR model in their articles to study the spread of epidemics from different perspectives (Valba et al, 2020;Vivekanandhan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Zhang et al. (2022) used the SI model to investigate the relationship between link closure strategies and traffic‐driven epidemic propagation. They all used the SIR model in their articles to study the spread of epidemics from different perspectives (Valba et al., 2020; Vivekanandhan et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Epidemic propagation risk study with effective fractal dimension

Nian

2023

Risk Analysis

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In this article, the risk of epidemic transmission on complex networks is studied from the perspective of effective fractal dimension. First, we introduce the method of calculating the effective fractal dimension of the network by taking a scale‐free network as an example. Second, we propose the construction method of administrative fractal network and calculate the . using the classical susceptible exposed infectious removed (SEIR) infectious disease model, we simulate the virus propagation process on the administrative fractal network. The results show that the larger the is, the higher the risk of virus transmission is. Later, we proposed five parameters P, M, B, F, and D, where P denotes population mobility, M denotes geographical distance, B denotes GDP, F denotes , and D denotes population density. The new epidemic growth index formula was obtained by combining these five parameters, and the validity of I in epidemic transmission risk assessment was demonstrated by parameter sensitivity analysis and reliability analysis. Finally, we also confirmed the reliability of the SEIR dynamic transmission model in simulating early COVID‐19 transmission trends and the ability of timely quarantine measures to effectively control the spread of the epidemic.

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Traffic-driven SIR epidemic spread dynamics on scale-free networks

Zhang

et al. 2023

Int. J. Mod. Phys. C

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Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible–Infected–Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.

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Traffic-driven SI epidemic spreading on scale-free networks

Cited by 3 publications

References 45 publications

Modeling Traffic Congestion Spreading Using a Topology-Based SIR Epidemic Model

Modeling Traffic Congestion Spreading Using a Topology-Based SIR Epidemic Model

Epidemic propagation risk study with effective fractal dimension

Traffic-driven SIR epidemic spread dynamics on scale-free networks

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