ArticleScientific reports2015
Information content of contact-pattern representations and predictability of epidemic outbreaks.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Static graph approximations of dynamic contact networks for epidemic forecasting.Scientific reports · 2024Article
- Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model.Scientific reports · 2023Article
- Concurrency measures in the era of temporal network epidemiology: a review.Journal of the Royal Society, Interface · 2021Review
- Article
- Concurrency and reachability in treelike temporal networks.Physical review. E · 2019Article
- Article
- Conceptualizing Geosexual Archetypes: Mapping the Sexual Travels and Egocentric Sexual Networks of Gay and Bisexual Men in Toronto, Canada.Sexually transmitted diseases · 2018Article
- The effects of heterogeneity on stochastic cycles in epidemics.Scientific reports · 2017Article
- A stochastic model for MRSA transmission within a hospital ward incorporating environmental contamination.Epidemiology and infection · 2017Article
- Article
- Graph distance for complex networks.Scientific reports · 2016Article
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Abstract
To understand the contact patterns of a population--who is in contact with whom, and when the contacts happen--is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can meet any with equal probability. A more modern approach, network epidemiology, assumes people are connected into a static network over which the disease spreads. Newer yet, temporal network epidemiology, includes the time in the contact representations. In this paper, we investigate the effect of these successive inclusions of more information. Using empirical proximity data, we study both outbreak sizes from unknown sources, and from known states of ongoing outbreaks. In the first case, there are large differences going from a fully mixed simulation to a network, and from a network to a temporal network. In the second case, differences are smaller. We interpret these observations in terms of the temporal network structure of the data sets. For example, a fast overturn of nodes and links seem to make the temporal information more important.
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