ArticleF1000prime reports2013
Predicting and controlling infectious disease epidemics using temporal networks.
Article in F1000prime reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed.
- A novel approach to estimatingFrontiers in public health · 2025Article
- An algorithm to build synthetic temporal contact networks based on close-proximity interactions data.PLoS computational biology · 2024Article
- Patterns in Temporal Networks with Higher-Order Egocentric Structures.Entropy (Basel, Switzerland) · 2024Article
- The role of epidemic spreading in seizure dynamics and epilepsy surgery.Network neuroscience (Cambridge, Mass.) · 2023Article
- Description of the Cattle and Small Ruminants Trade Network in Senegal and Implication for the Surveillance of Animal Diseases.Transboundary and emerging diseases · 2023Article
- Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings.Scientific reports · 2022Article
- Quantifying agent impacts on contact sequences in social interactions.Scientific reports · 2022Article
- Risk-aware temporal cascade reconstruction to detect asymptomatic cases.Knowledge and information systems · 2022Article
- An integrated simulation framework for the prevention and mitigation of pandemics caused by airborne pathogens.Network modeling and analysis in health informatics and bioinformatics · 2022Article
- Revealing mechanisms of infectious disease spread through empirical contact networks.PLoS computational biology · 2021Article
- Analyzing spatial mobility patterns with time-varying graphical lasso: Application to COVID-19 spread.Transactions in GIS : TG · 2021Article
- Concurrency measures in the era of temporal network epidemiology: a review.Journal of the Royal Society, Interface · 2021Review
- Article
- Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19.International journal of environmental research and public health · 2020Article
- Illustration of Different Disease Transmission Routes in a Pig Trade Network by Monopartite and Bipartite Representation.Animals : an open access journal from MDPI · 2020Article
- Article
- Particle velocity controls phase transitions in contagion dynamics.Scientific reports · 2019Article
- Transmission on empirical dynamic contact networks is influenced by data processing decisions.Epidemics · 2019Article
- Fitness preferential attachment as a driving mechanism in bitcoin transaction network.PloS one · 2019Article
- Spreading dynamics in a cattle trade network: Size, speed, typical profile and consequences on epidemic control strategies.PloS one · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.
Identifiers
What OpenQuestion holds
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.