ArticlePLoS computational biology2013
Epidemiologically optimal static networks from temporal network data.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- REPLAY: a GPU-accelerated tool for temporal contact network epidemiology.BMC medical informatics and decision making · 2025Article
- Static graph approximations of dynamic contact networks for epidemic forecasting.Scientific reports · 2024Article
- Compressing the Chronology of a Temporal Network with Graph Commutators.Physical review letters · 2024Article
- Constructing temporal networks with bursty activity patterns.Nature communications · 2023Article
- Trading contact tracing efficiency for finding patient zero.Scientific reports · 2022Article
- Predicting network dynamics without requiring the knowledge of the interaction graph.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- From temporal network data to the dynamics of social relationships.Proceedings. Biological sciences · 2021Article
- Concurrency measures in the era of temporal network epidemiology: a review.Journal of the Royal Society, Interface · 2021Review
- COVID-19 and Networks.New generation computing · 2021Article
- The role of social structure and dynamics in the maintenance of endemic disease.Behavioral ecology and sociobiology · 2021Review
- Transmission on empirical dynamic contact networks is influenced by data processing decisions.Epidemics · 2019Article
- Entropy Measures of Human Communication Dynamics.Scientific reports · 2018Article
- Epidemic Threshold in Continuous-Time Evolving Networks.Physical review letters · 2018Article
- Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks.Scientific reports · 2018Article
- Cost-efficient vaccination protocols for network epidemiology.PLoS computational biology · 2017Article
- Network analysis of pig movements: Loyalty patterns and contact chains of different holding types in Denmark.PloS one · 2017Article
- Diffusion archeology for diffusion progression history reconstruction.Knowledge and information systems · 2016Article
- Graph distance for complex networks.Scientific reports · 2016Article
- Impact of Network Activity on the Spread of Infectious Diseases through the German Pig Trade Network.Frontiers in veterinary science · 2016Article
- Information content of contact-pattern representations and predictability of epidemic outbreaks.Scientific reports · 2015Article
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1 author.
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Abstract
One of network epidemiology's central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets.
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