ArticleScientific reports2016
Individual-based approach to epidemic processes on arbitrary dynamic contact networks.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 40 citations in OpenAlex.
- REPLAY: a GPU-accelerated tool for temporal contact network epidemiology.BMC medical informatics and decision making · 2025Article
- Active querying approach to epidemic source detection on contact networks.Scientific reports · 2023Article
- Direct modelling from GPS data reveals daily-activity-dependency of effective reproduction number in COVID-19 pandemic.Scientific reports · 2022Article
- Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey.PLoS computational biology · 2022Article
- Dengue illness impacts daily human mobility patterns in Iquitos, Peru.PLoS neglected tropical diseases · 2019Article
- Big Data Analytics, Infectious Diseases and Associated Ethical Impacts.Philosophy & technology · 2019Article
- Effective Methods of Restraining Diffusion in Terms of Epidemic Dynamics.Scientific reports · 2017Article
- Percolation-theoretic bounds on the cache size of nodes in mobile opportunistic networks.Scientific reports · 2017Article
- Voluntary Vaccination through Self-organizing Behaviors on Locally-mixed Social Networks.Scientific reports · 2017Article
- Matching theory and evidence on Covid-19 using a stochastic network SIR model.Journal of applied econometrics (Chichester, England)Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dynamics of contact networks and epidemics of infectious diseases often occur on comparable time scales. Ignoring one of these time scales may provide an incomplete understanding of the population dynamics of the infection process. We develop an individual-based approximation for the susceptible-infected-recovered epidemic model applicable to arbitrary dynamic networks. Our framework provides, at the individual-level, the probability flow over time associated with the infection dynamics. This computationally efficient framework discards the correlation between the states of different nodes, yet provides accurate results in approximating direct numerical simulations. It naturally captures the temporal heterogeneities and correlations of contact sequences, fundamental ingredients regulating the timing and size of an epidemic outbreak, and the number of secondary infections. The high accuracy of our approximation further allows us to detect the index individual of an epidemic outbreak in real-life network data.
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Registered trials
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.