ArticleScientific reports2019
Network properties of salmonella epidemics.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 9 citations in OpenAlex.
- Modelling transmission and control of the COVID-19 pandemic in Australia.Nature communications · 2020Article
- Inferring evolutionary pathways and directed genotype networks of foodborne pathogens.PLoS computational biology · 2020Article
- Phase Transitions in Spatial Connectivity during Influenza Pandemics.Entropy (Basel, Switzerland) · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We examine non-typhoidal Salmonella (S. Typhimurium or STM) epidemics as complex systems, driven by evolution and interactions of diverse microbial strains, and focus on emergence of successful strains. Our findings challenge the established view that seasonal epidemics are associated with random sets of co-circulating STM genotypes. We use high-resolution molecular genotyping data comprising 17,107 STM isolates representing nine consecutive seasonal epidemics in Australia, genotyped by multiple-locus variable-number tandem-repeats analysis (MLVA). From these data, we infer weighted undirected networks based on distances between the MLVA profiles, depicting epidemics as networks of individual bacterial strains. The network analysis demonstrated dichotomy in STM populations which split into two distinct genetic branches, with markedly different prevalences. This distinction revealed the emergence of dominant STM strains defined by their local network topological properties, such as centrality, while correlating the development of new epidemics with global network features, such as small-world propensity.
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.