Epidemic spread on patch networks with community structure

Lieberthal, Brandon; Soliman, Abdel-Hamid; Wang, Shaowen; Urioste-Stone, Sandra De; Gardner, Allison

doi:10.1016/j.mbs.2023.108996

Cited by 5 publications

(2 citation statements)

References 80 publications

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“…Other network properties-such as efficiency [31], assortativity [32], directionality [33] or spatial aspects [34]-may also be as relevant as degree heterogeneity. A well-defined community structure, in particular, can have an important effect [35]. Ultimately, epidemics are yet another example of how the architecture of complex systems is fundamental to their dynamical behaviour [10,11,21].…”

Section: Discussionmentioning

confidence: 99%

Epidemic modelling requires knowledge of the social network

Johnson

2024

J. Phys. Complex.

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‘Compartmental models’ of epidemics are widely used to forecast the effects of communicable diseases such as COVID-19 and to guide policy. Although it has long been known that such processes take place on social networks, the assumption of ‘random mixing’ is usually made, which ignores network structure. However, ‘super-spreading events’ have been found to be power-law distributed, suggesting that the underlying networks may be scale free or at least highly heterogeneous. The random-mixing assumption would then produce an overestimation of the herd-immunity threshold for given R 0; and a (more significant) overestimation of R 0 itself. These two errors compound each other, and can lead to forecasts greatly overestimating the number of infections. Moreover, if networks are heterogeneous and change in time, multiple waves of infection can occur, which are not predicted by random mixing. A simple SIR model simulated on both Erdős–Rényi and scale-free networks shows that details of the network structure can be more important than the intrinsic transmissibility of a disease. It is therefore crucial to incorporate network information into standard models of epidemics.

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Section: Discussionmentioning

confidence: 99%

Epidemic modelling requires knowledge of the social network

Johnson

2024

J. Phys. Complex.

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“…Several studies have investigated the impact of network structure and community dynamics on disease spread and control in complex networks. In their work on epidemic spread on patch networks with community structure, researchers 13 investigate the influence of community structure on the spread of diseases within human metapopulation networks. They highlight the significant impact of community structures on disease reproduction rates, emphasizing the importance of mitigation strategies such as movement restrictions and vaccinations.…”

Section: Related Workmentioning

confidence: 99%

Minimizing outbreak through targeted blocking for disease control: a community-based approach using super-spreader node identification

Sheikhahmadi,

Bahrami,

Saremi

2023

Sci Rep

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The COVID-19 pandemic has caused significant disruptions to the daily lives of individuals worldwide, with many losing their lives to the virus. Vaccination has been identified as a crucial strategy to combat the spread of a disease, but with a limited supply of vaccines, targeted blocking is becoming increasingly necessary. One such approach is to block a select group of individuals in the community to control the spread of the disease in its early stages. Therefore, in this paper, a method is proposed for solving this problem, based on the similarity between this issue and the problem of identifying super-spreader nodes. The proposed method attempts to select the minimum set of network nodes that, when removed, no large component remains in the network. To this end, the network is partitioned into various communities, and a method for limiting the spread of the disease to communities is proposed by blocking connecting nodes. Four real networks and four synthetics networks created using the LFR algorithm were used to evaluate the control of the disease by the selected set of nodes using each method, and the results obtained indicate better performance of the proposed method compared to other methods.

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