Editing Phylogenetics (section)

=== Infectious disease epidemiology ===
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[[Whole genome sequencing|Whole-genome sequence]] data from outbreaks or epidemics of infectious diseases can provide important insights into transmission dynamics and inform public health strategies. Traditionally, studies have combined genomic and epidemiological data to reconstruct transmission events. However, recent research has explored deducing transmission patterns solely from genomic data using [[phylodynamics]], which involves analyzing the properties of pathogen phylogenies. Phylodynamics uses theoretical models to compare predicted branch lengths with actual branch lengths in phylogenies to infer transmission patterns. Additionally, [[coalescent theory]], which describes probability distributions on trees based on population size, has been adapted for epidemiological purposes. Another source of information within phylogenies that has been explored is "tree shape." These approaches, while computationally intensive, have the potential to provide valuable insights into pathogen transmission dynamics.<ref name="Colijn-2014">{{Cite journal |last1=Colijn |first1=Caroline |last2=Gardy |first2=Jennifer |date=2014-01-01 |title=Phylogenetic tree shapes resolve disease transmission patterns |url=https://academic.oup.com/emph/article/2014/1/96/1845716 |journal=Evolution, Medicine, and Public Health |language=en |volume=2014 |issue=1 |pages=96–108 |doi=10.1093/emph/eou018 |issn=2050-6201 |pmc=4097963 |pmid=24916411}}</ref>
[[File:WebTree.jpg|thumb|320x320px|Pathogen Transmission Trees]]

The structure of the host contact network significantly impacts the dynamics of outbreaks, and management strategies rely on understanding these transmission patterns. Pathogen genomes spreading through different contact network structures, such as chains, homogeneous networks, or networks with super-spreaders, accumulate mutations in distinct patterns, resulting in noticeable differences in the shape of phylogenetic trees, as illustrated in Fig. 1. Researchers have analyzed the structural characteristics of phylogenetic trees generated from simulated bacterial genome evolution across multiple types of contact networks. By examining simple topological properties of these trees, researchers can classify them into chain-like, homogeneous, or super-spreading dynamics, revealing transmission patterns. These properties form the basis of a computational classifier used to analyze real-world outbreaks. Computational predictions of transmission dynamics for each outbreak often align with known epidemiological data.
[[File:GraphRepresentation.jpg|thumb|350x350px|Graphical Representation of Phylogenetic Tree analysis]]
Different transmission networks result in quantitatively different tree shapes. To determine whether tree shapes captured information about underlying disease transmission patterns, researchers simulated the evolution of a bacterial genome over three types of outbreak contact networks—homogeneous, super-spreading, and chain-like. They summarized the resulting phylogenies with five metrics describing tree shape. Figures 2 and 3 illustrate the distributions of these metrics across the three types of outbreaks, revealing clear differences in tree topology depending on the underlying host contact network.

Super-spreader networks give rise to phylogenies with higher Colless imbalance, longer ladder patterns, lower Δw, and deeper trees than those from homogeneous contact networks. Trees from chain-like networks are less variable, deeper, more imbalanced, and narrower than those from other networks.

Scatter plots can be used to visualize the relationship between two variables in pathogen transmission analysis, such as the number of infected individuals and the time since infection. These plots can help identify trends and patterns, such as whether the spread of the pathogen is increasing or decreasing over time, and can highlight potential transmission routes or super-spreader events. [[Box plot]]s displaying the range, median, quartiles, and potential outliers datasets can also be valuable for analyzing pathogen transmission data, helping to identify important features in the data distribution. They may be used to quickly identify differences or similarities in the transmission data.<ref name="Colijn-2014" />