ArticleJournal of mathematical biology2013
A network with tunable clustering, degree correlation and degree distribution, and an epidemic thereon.
Article in Journal of mathematical biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Simulating contact networks for livestock disease epidemiology: a systematic review.Journal of the Royal Society, Interface · 2023Pooled it
- Targeted vaccination is effective for mpox clade Ib in England despite increased household transmission: Predictions from a modelling study.PLOS global public health · 2025Article
- Understanding dynamics and overlapping epidemiologies of HIV, HSV-2, chlamydia, gonorrhea, and syphilis in sexual networks of men who have sex with men.Frontiers in public health · 2024Article
- Concurrency measures in the era of temporal network epidemiology: a review.Journal of the Royal Society, Interface · 2021Review
- Dynamics of COVID-19 under social distancing measures are driven by transmission network structure.medRxiv : the preprint server for health sciences · 2021Article
- SIR dynamics in random networks with communities.Journal of mathematical biology · 2018Article
- Sexual network drivers of HIV and herpes simplex virus type 2 transmission.AIDS (London, England) · 2017Article
- Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks.PLoS computational biology · 2016Article
- Disease invasion risk in a growing population.Journal of mathematical biology · 2016Article
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Authors and funding
3 authors.
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Abstract
A random network model which allows for tunable, quite general forms of clustering, degree correlation and degree distribution is defined. The model is an extension of the configuration model, in which stubs (half-edges) are paired to form a network. Clustering is obtained by forming small completely connected subgroups, and positive (negative) degree correlation is obtained by connecting a fraction of the stubs with stubs of similar (dissimilar) degree. An SIR (Susceptible --> Infective --> Recovered) epidemic model is defined on this network. Asymptotic properties of both the network and the epidemic, as the population size tends to infinity, are derived: the degree distribution, degree correlation and clustering coefficient, as well as a reproduction number R(*), the probability of a major outbreak and the relative size of such an outbreak. The theory is illustrated by Monte Carlo simulations and numerical examples. The main findings are that (1) clustering tends to decrease the spread of disease, (2) the effect of degree correlation is appreciably greater when the disease is close to threshold than when it is well above threshold and (3) disease spread broadly increases with degree correlation ρ when R(*) is just above its threshold value of one and decreases with ρ when R(*) is well above one.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.