Ball, F., Britton, T., & Neal, P. (2024). An epidemic model on a network having two group structures with tunable overlap. arXiv preprint arXiv:2410.06696.
This paper investigates the impact of varying degrees of overlap between household and workplace structures on the probability and size of major outbreaks in an SIR epidemic model. The study aims to determine how the parameter θ, representing the probability of an individual belonging to different households and workplaces, affects epidemic dynamics.
The authors develop a stochastic continuous-time SIR epidemic model on a network with two group structures: households and workplaces. The model incorporates a tunable parameter (θ) to control the degree of overlap between these structures. They analyze the model in the asymptotic regime as the population size (n) approaches infinity, considering scenarios with and without global infection. The analysis utilizes branching process approximations, local susceptibility sets, and local infectious clumps to derive the probability and size of major outbreaks.
The research demonstrates the importance of considering the overlap between different social structures when modeling epidemic spread. The findings suggest that promoting social structures with higher overlap, such as assigning students to dorms based on their classes, could potentially mitigate the severity of outbreaks.
This study contributes valuable insights to the field of epidemic modeling by providing a rigorous mathematical framework for analyzing the impact of overlapping group structures. The findings have practical implications for public health interventions, particularly in understanding how social organization can influence disease transmission.
The model assumes constant household and workplace sizes, which may not reflect real-world scenarios. Future research could explore the effects of variable group sizes and more complex network structures on epidemic dynamics. Additionally, incorporating individual heterogeneity in susceptibility and infectivity could enhance the model's realism.
To Another Language
from source content
arxiv.org
Önemli Bilgiler Şuradan Elde Edildi
by Frank Ball, ... : arxiv.org 10-10-2024
https://arxiv.org/pdf/2410.06696.pdfDaha Derin Sorular