ArticlePloS one2011
Evolution of scaling emergence in large-scale spatial epidemic spreading.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 81 citations in OpenAlex.
- The Relationship between the Migrant Population's Migration Network and the Risk of COVID-19 Transmission in China-Empirical Analysis and Prediction in Prefecture-Level Cities.International journal of environmental research and public health · 2020Article
- Disease spreading in complex networks: A numerical study with Principal Component Analysis.Expert systems with applications · 2018Article
- Identifying Spatial Invasion of Pandemics on Metapopulation Networks Via Anatomizing Arrival History.IEEE transactions on cybernetics · 2016Article
- Incorporating Human Movement Behavior into the Analysis of Spatially Distributed Infrastructure.PloS one · 2016Article
- Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa.PLoS computational biology · 2015Article
- Effects of time delay and space on herbivore dynamics: linking inducible defenses of plants to herbivore outbreak.Scientific reports · 2015Article
- Cascading walks model for human mobility patterns.PloS one · 2015Article
- Distributed clone detection in static wireless sensor networks: random walk with network division.PloS one · 2015Article
- Freezing period strongly impacts the emergence of a global consensus in the voter model.Scientific reports · 2014Article
- Spatial epidemiology of networked metapopulation: an overview.Chinese science bulletin = Kexue tongbao · 2014Article
- Correlations and scaling laws in human mobility.PloS one · 2014Article
- Immunization of epidemics in multiplex networks.PloS one · 2014Article
- A two-step high-risk immunization based on high-risk immunization.Nonlinear dynamics · 2014Article
- Effects of delayed recovery and nonuniform transmission on the spreading of diseases in complex networks.Physica A · 2013Article
- How human location-specific contact patterns impact spatial transmission between populations?Scientific reports · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundZipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which has still hardly been clarified. METHODOLOGY/PRINCIPAL
findingsIn this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. CONCLUSIONS/SIGNIFICANCE: The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.
Indexed as
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.