ArticleStatistics in medicine2018
Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Network Models for HIV Spread.Journal of acquired immune deficiency syndromes (1999) · 2025Pooled it
- A graph homomorphism approach for unraveling histories of metastatic cancers and viral outbreaks under evolutionary constraints.Nature communications · 2025Article
- Estimating contact network properties by integrating multiple data sources associated with infectious diseases.Statistics in medicine · 2023Article
- Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection.Proceedings. Biological sciences · 2021Article
- "Stay nearby or get checked": A Covid-19 control strategy.Infectious Disease Modelling · 2021Article
- Identification of male infertility-related long non-coding RNAs and their functions based on a competing endogenous RNA network.The Journal of international medical research · 2020Article
- Assessing biases in phylodynamic inferences in the presence of super-spreaders.Veterinary research · 2019Article
- Phylogenies from dynamic networks.PLoS computational biology · 2019Article
- Detection of HIV transmission clusters from phylogenetic trees using a multi-state birth-death model.Journal of the Royal Society, Interface · 2018Article
- Incorporating genomic methods into contact networks to reveal new insights into animal behavior and infectious disease dynamics.Behaviour · 2018Article
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Abstract
Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute-based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright © 2017 John Wiley & Sons, Ltd.
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