What does this research mean for the field?

In individual-based models of SIS epidemic propagation, the mean-field approximation is highly accurate for dense random regular graphs, but for sparse graphs it requires a correction term that depends linearly on the mean distance in the network. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to understand the effect of network structure on epidemic propagation dynamics using an individual-based model.

June 20, 2026Electronic journal of qualitative theory of differential equationsOpen Access

The effect of network structure on the dynamics of epidemic propagation in an individual-based model

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Authors

NNNoémi NagyBudapest University of Technology and Economics SHSándor HorváthEötvös Loránd University PSP. SimonEötvös Loránd University

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Implication

Randomized trial explores epidemic dynamics influenced by network structure, suggesting adjustments for sparse graphs.

Key Points

This study aims to understand the effect of network structure on epidemic propagation dynamics using an individual-based model.
Utilized an individual-based model for SIS epidemic propagation on regular graphs.
Incorporated the adjacency matrix of the network to analyze propagation dynamics.
Examined the performance of the mean-field approximation in relation to network characteristics.
For dense random regular graphs, the mean-field approximation performed excellently.
Sparse graphs required a significant correction term for accurate time dependence of prevalence.
Correction term was shown to depend linearly on the mean distance within the network.

Cite This Study

Nagy et al. (2026) studied this question.

synapsesocial.com/papers/6a362fbcdb0793dc1a53727e https://doi.org/10.14232/ejqtde.2026.1.10

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The effect of network structure on the dynamics of epidemic propagation in an individual-based model

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Key Points

Cite This Study

The effect of network structure on the dynamics of epidemic propagation in an individual-based model

Discussion

Member takes

Implication

Key Points

Cite This Study

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