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Network properties of salmonella epidemics
Oliver M Cliff1, Vitali Sintchenko2,3, Tania C Sorrell2,3
1Centre for Complex Systems, Faculty of Engineering and IT, University of Sydney, Sydney, NSW, 2006, Australia.
Scientific Reports
|April 18, 2019
Summary
Non-typhoidal Salmonella (STM) epidemics are complex systems, not random. Network analysis revealed two distinct genetic branches and the emergence of dominant STM strains, challenging previous views on seasonal outbreaks.
Area of Science:
- Microbial Evolution
- Epidemiology
- Complex Systems Analysis
Background:
- Non-typhoidal Salmonella (STM) epidemics are traditionally viewed as seasonal outbreaks with random co-circulating genotypes.
- Understanding the evolutionary dynamics and strain interactions driving epidemic success is crucial.
Purpose of the Study:
- To investigate the complex system dynamics of non-typhoidal Salmonella (S. Typhimurium or STM) epidemics.
- To identify the factors contributing to the emergence and success of dominant STM strains.
- To challenge the established view of random genotype sets in seasonal epidemics.
Main Methods:
- Utilized high-resolution molecular genotyping data from 17,107 STM isolates across nine Australian seasonal epidemics.
- Employed multiple-locus variable-number tandem-repeats analysis (MLVA) for strain genotyping.
- Inferred weighted undirected networks based on MLVA profile distances to model epidemic dynamics.
Main Results:
- Network analysis revealed a dichotomy in STM populations, splitting into two distinct genetic branches with differing prevalences.
- Identified the emergence of dominant STM strains characterized by specific local network topological properties (e.g., centrality).
- Correlated the development of new epidemics with global network features, such as small-world propensity.
Conclusions:
- STM epidemics exhibit complex system behavior, driven by distinct genetic lineages rather than random genotype associations.
- Dominant STM strains emerge based on their network positions and topological properties.
- Network analysis provides novel insights into the drivers of seasonal Salmonella epidemics.
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