ArticlePhysical review letters2018
Epidemic Threshold in Continuous-Time Evolving Networks.
Article in Physical review letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Compressing the Chronology of a Temporal Network with Graph Commutators.Physical review letters · 2024Article
- Epidemic graph diagrams as analytics for epidemic control in the data-rich era.Nature communications · 2023Article
- Epidemic spreading with migration in networked metapopulation.Communications in nonlinear science & numerical simulation · 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
- Differences in social activity increase efficiency of contact tracing.The European physical journal. B · 2021Article
- Equivalence and its invalidation between non-Markovian and Markovian spreading dynamics on complex networks.Nature communications · 2019Article
- Consensus and polarization in competing complex contagion processes.Journal of the Royal Society, Interface · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current understanding of the critical outbreak condition on temporal networks relies on approximations (time scale separation, discretization) that may bias the results. We propose a theoretical framework to compute the epidemic threshold in continuous time through the infection propagator approach. We introduce the weak commutation condition allowing the interpretation of annealed networks, activity-driven networks, and time scale separation into one formalism. Our work provides a coherent connection between discrete and continuous time representations applicable to realistic scenarios.
Identifiers
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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.