SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Skums, Pavel; Mohebbi, Fatemeh; Tsyvina, Vyacheslav; Baykal, Pelin Icer; Nemira, Alina; Ramachandran, Sumathi; Khudyakov, Yury

doi:10.1016/j.cels.2022.07.005

Cited by 5 publications

(3 citation statements)

References 70 publications

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“…The host contact network is modeled as an undirected, unweighted graph, with nodes representing hosts and edges indicating contacts between them (Figure 2A, top). In e3SIM, transmissions occur only between directly connected hosts, making the contact network crucial for modeling outbreak dynamics [43]. The network structure remains static throughout the simulation.…”

Section: Host Population Contact Networkmentioning

confidence: 99%

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Xu,

Liang,

Hahn

et al. 2024

Preprint

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Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogen's genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIM's capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

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Section: Host Population Contact Networkmentioning

confidence: 99%

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Xu,

Liang,

Hahn

et al. 2024

Preprint

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“…It does not explicitly model the bottleneck and was tested only on simulations where full sampling is assumed. Finally, there have been recent advancements in highly scalable models for transmission network inference, when taking into account the within host pathogen diversity (Skums et al 2022; Specht et al 2023; Carson et al 2024). However, they either require a time scaled phylogeny as an input (Skums et al 2022; Carson et al 2024), thus potentially limiting the full exploration of phylogenetic uncertainties, or are non-tree based (Specht et al 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Finally, there have been recent advancements in highly scalable models for transmission network inference, when taking into account the within host pathogen diversity (Skums et al 2022; Specht et al 2023; Carson et al 2024). However, they either require a time scaled phylogeny as an input (Skums et al 2022; Carson et al 2024), thus potentially limiting the full exploration of phylogenetic uncertainties, or are non-tree based (Specht et al 2023). Moreover, a common limitation among these models is their inability to explicitly model unobserved transmission events or to accurately infer the timing of such events.…”

Section: Introductionmentioning

confidence: 99%

Integrating Transmission Dynamics and Pathogen Evolution Through a Bayesian Approach

Stolz,

Stadler,

Vaughan

2024

Preprint

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The collection of pathogen samples and subsequent genetic sequencing enables the reconstruction of phylogenies, shedding light on transmission dynamics. However, many existing phylogenetic methods fall short by neglecting within-host diversity and the impact of transmission bottlenecks, leading to inaccuracies in understanding epidemic spread. This paper introduces the Transmission Tree (TnT) model, which leverages multiple pathogen gene trees to more accurately model transmission history. By extending the Bayesian phylogenetic analysis software BEAST2, TnT integrates the sampled ancestor birth-death model for transmission trees and the multi-species coalescent model for pathogen gene trees. This integration allows for the consideration of critical factors like transmission orientation, incomplete lineage sorting, and within- and between-host diversity. Notably, TnT incorporates an analytical approach to address unobserved transmission events, crucial in scenarios with incomplete sampling. Through theoretical evaluation and application to a real-world HIV transmission chain, we demonstrate that TnT offers a robust solution to improve understanding of epidemic dynamics by effectively combining pathogen gene sequences and clinical data.

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Graph-Based Reconstruction and Analysis of Disease Transmission Networks Using Viral Genomic Data

Vikalo

2023

Journal of Computational Biology

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Understanding the patterns of viral disease transmissions helps establish public health policies and aids in controlling and ending a disease outbreak. Classical methods for studying disease transmission dynamics that rely on epidemiological data, such as times of sample collection and duration of exposure intervals, struggle to provide desired insight due to limited informativeness of such data. A more precise characterization of disease transmissions may be acquired from sequencing data that reveals genetic distance between viral populations in patient samples. Indeed, genetic distance between viral strains present in hosts contains valuable information about transmission history, thus motivating the design of methods that rely on genomic data to reconstruct a directed disease transmission network, detect transmission clusters, and identify significant network nodes (e.g., super-spreaders). In this paper, we present a novel end-to-end framework for the analysis of viral transmissions utilizing viral genomic (sequencing) data. The proposed framework groups infected hosts into transmission clusters based on reconstructed viral quasispecies; the genetic distance between a pair of hosts is calculated using Earth Mover's Distance, and further used to infer transmission direction between the hosts. To quantify the significance of a host in the transmission network, the importance score is calculated by a graph convolutional auto-encoder. The viral transmission network is represented by a directed minimum spanning tree utilizing the Edmond's algorithm modified to incorporate constraints on the importance scores of the hosts. Results on realistic synthetic as well as experimental data demonstrate that the proposed framework outperforms state-of-the-art techniques for the analysis of viral transmission dynamics.

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SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Cited by 5 publications

References 70 publications

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Integrating Transmission Dynamics and Pathogen Evolution Through a Bayesian Approach

Graph-Based Reconstruction and Analysis of Disease Transmission Networks Using Viral Genomic Data

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