Phylogenetic tree shapes resolve disease transmission patterns

Abstract

A B S T R A C TBackground and Objectives: Whole-genome sequencing is becoming popular as a tool for understanding outbreaks of communicable diseases, with phylogenetic trees being used to identify individual transmission events or to characterize outbreak-level overall transmission dynamics. Existing methods to infer transmission dynamics from sequence data rely on well-characterized infectious periods, epidemiological and clinical metadata which may not always be available, and typically require computationally intensive analysis focusing on the branch lengths in phylogenetic trees. We sought to determine whether the topological structures of phylogenetic trees contain signatures of the transmission patterns underlying an outbreak.Methodology: We use simulated outbreaks to train and then test computational classifiers. We test the method on data from two real-world outbreaks. Results:We show that different transmission patterns result in quantitatively different phylogenetic tree shapes. We describe topological features that summarize a phylogeny's structure and find that computational classifiers based on these are capable of predicting an outbreak's transmission dynamics. The method is robust to variations in the transmission parameters and network types, and recapitulates known epidemiology of previously characterized real-world outbreaks. Conclusions and implications:There are simple structural properties of phylogenetic trees which, when combined, can distinguish communicable disease outbreaks with a super-spreader, homogeneous transmission and chains of transmission. This is possible using genome data alone, and can be done during an outbreak. We discuss the implications for management of outbreaks.

Tactics and Strategies for Managing Ebola Outbreaks and the Salience of Immunization

“…In addition, they allow us to construct offspring distributions for different phases of the epidemic across different realizations and transmission tree structures [ 22 , 23 ] that might provide clues to the role of superspreaders [ 14 ], or other heterogeneities in the susceptibility and infectiousness of individuals (e.g., deceased patients and unsafe burial ceremony). Information on offspring distributions can also be used to help fit models to single outbreaks [ 24 ].…”

“…With the rapidly increasing power of genetic sequencing methodologies, transmission trees can be constructed for viral pathogens, such as EBOV [ 25 ], using genetic data [ 12 , 22 , 26 , 27 ]. Thus, despite being a very challenging problem, the key to fitting a stochastic process model to a single realization, represented by a particular outbreak, appears to be rooted in fitting the model to the associated offspring and phylogenetic tree distributions that emerge and that better characterize the actual process [ 23 , 24 ] than the much more variable case size or R 0 statistic. It has recently been reported that EBOV genomes were sequenced, using blood samples from 78 patients in Sierra Leone [ 25 ].…”

Distance metrics for ranked evolutionary trees

“…These parameters capture two phenomena that have been widely used for phylogenetic studies. The parameter β ∈ [−2, ∞), as in the beta-splitting model (37), determines the degree of balance, an important tree attribute that car-ries information on underlying evolutionary or epidemiological processes, including speciation and extinction (38)(39)(40)(41), natural selection (42)(43)(44)(45), and infectious disease transmission (46)(47)(48). The other parameter α ∈ (−∞, ∞) regulates the relationship between clade family size and proximity to the root (clade sizeage relation), which is fundamental to understanding the effects of ecological, evolutionary, geographic, and other factors on biodiversity (7,(49)(50)(51)(52).…”

Predicting the Underlying Structure for Phylogenetic Trees Using Neural Networks and Logistic Regression

“…Other definitions for ladder length, maximum depth of a tree, maximum width and maximum width over maximum depth can be found in [17]. The implementation of phylogenetic tree simulation and computation of tree statistics were implemented in Python software, version 3.7.3.…”