Samuel L Hong1, Philippe Lemey1, Marc A Suchard2,3,4, Guy Baele1. 1. Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Laboratory of Clinical and Evolutionary Virology, Leuven, Belgium. 2. Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California. 3. Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California. 4. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Abstract
Advances in sequencing technologies have tremendously reduced the time and costs associated with sequence generation, making genomic data an important asset for routine public health practices. Within this context, phylogenetic and phylogeographic inference has become a popular method to study disease transmission. In a Bayesian context, these approaches have the benefit of accommodating phylogenetic uncertainty, and popular implementations provide the possibility to parameterize the transition rates between locations as a function of epidemiological and ecological data to reconstruct spatial spread while simultaneously identifying the main factors impacting the spatial spread dynamics. Recent developments enable researchers to make use of travel history data of infected individuals in the reconstruction of pathogen spread, offering increased inference accuracy and mitigating sampling bias. Here, we describe a detailed workflow to reconstruct the spatial spread of a pathogen through Bayesian phylogeographic analysis in discrete space using these novel approaches, implemented in BEAST. The individual protocols focus on how to incorporate molecular data, covariates of spread, and individual travel history data into the analysis.
Advances in sequencing technologies have tremendously reduced the time and costs associated with sequence generation, making genomic data an important asset for routine public health practices. Within this context, phylogenetic and phylogeographic inference has become a popular method to study disease transmission. In a Bayesian context, these approaches have the benefit of accommodating phylogenetic uncertainty, and popular implementations provide the possibility to parameterize the transition rates between locations as a function of epidemiological and ecological data to reconstruct spatial spread while simultaneously identifying the main factors impacting the spatial spread dynamics. Recent developments enable researchers to make use of travel history data of infected individuals in the reconstruction of pathogen spread, offering increased inference accuracy and mitigating sampling bias. Here, we describe a detailed workflow to reconstruct the spatial spread of a pathogen through Bayesian phylogeographic analysis in discrete space using these novel approaches, implemented in BEAST. The individual protocols focus on how to incorporate molecular data, covariates of spread, and individual travel history data into the analysis.
Authors: Philippe Lemey; Andrew Rambaut; Trevor Bedford; Nuno Faria; Filip Bielejec; Guy Baele; Colin A Russell; Derek J Smith; Oliver G Pybus; Dirk Brockmann; Marc A Suchard Journal: PLoS Pathog Date: 2014-02-20 Impact factor: 6.823
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Authors: Phuoc Truong Nguyen; Ravi Kant; Frederik Van den Broeck; Maija T Suvanto; Hussein Alburkat; Jenni Virtanen; Ella Ahvenainen; Robert Castren; Samuel L Hong; Guy Baele; Maarit J Ahava; Hanna Jarva; Suvi Tuulia Jokiranta; Hannimari Kallio-Kokko; Eliisa Kekäläinen; Vesa Kirjavainen; Elisa Kortela; Satu Kurkela; Maija Lappalainen; Hanna Liimatainen; Marc A Suchard; Sari Hannula; Pekka Ellonen; Tarja Sironen; Philippe Lemey; Olli Vapalahti; Teemu Smura Journal: Commun Med (Lond) Date: 2022-06-10