ArticlePLoS computational biology2013
Inferring the source of transmission with phylogenetic data.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Epidemic thresholds across sexual behavior groups in the UK Biobank.Frontiers in reproductive health · 2026Article
- Ethics of Identifying Individuals Involved in HIV Transmission Events by Phylogenetics in Molecular Surveillance.Bioethics · 2025Article
- Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates.Statistical methods in medical research · 2025Article
- Lessons learned from community engagement regarding phylodynamic research with molecular HIV surveillance data.Journal of the International AIDS Society · 2023Article
- Colonization and transmission ofMicrobial genomics · 2023Article
- Using genetic data to identify transmission risk factors: Statistical assessment and application to tuberculosis transmission.PLoS computational biology · 2022Article
- Molecular source attribution.PLoS computational biology · 2022Article
- Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Comparison of observer based methods for source localisation in complex networks.Scientific reports · 2022Article
- Article
- Sample size calculation for phylogenetic case linkage.PLoS computational biology · 2021Article
- Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data.eLife · 2021Article
- Concordance of HIV transmission risk factors elucidated using viral diversification rate and phylogenetic clustering.Evolution, medicine, and public health · 2021Article
- Correlates of hepatitis C viral clustering among people who inject drugs in Baltimore.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2020Article
- HIV-1 Transmission Patterns in Men Who Have Sex with Men: Insights from Genetic Source Attribution Analysis.AIDS research and human retroviruses · 2019Article
- PANGEA-HIV 2: Phylogenetics And Networks for Generalised Epidemics in Africa.Current opinion in HIV and AIDS · 2019Review
- HIV-1 genotype diversity and distribution characteristics among heterosexually transmitted population in Jiangsu province, China.Virology journal · 2019Article
- Phylogenies from dynamic networks.PLoS computational biology · 2019Article
- Evolutionary Virology at 40.Genetics · 2018Review
- Introduction of Phylodynamics for Controlling the HIV/AIDS Epidemic in Korea.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2018Article
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2 authors.
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Abstract
Identifying the source of transmission using pathogen genetic data is complicated by numerous biological, immunological, and behavioral factors. A large source of error arises when there is incomplete or sparse sampling of cases. Unsampled cases may act as either a common source of infection or as an intermediary in a transmission chain for hosts infected with genetically similar pathogens. It is difficult to quantify the probability of common source or intermediate transmission events, which has made it difficult to develop statistical tests to either confirm or deny putative transmission pairs with genetic data. We present a method to incorporate additional information about an infectious disease epidemic, such as incidence and prevalence of infection over time, to inform estimates of the probability that one sampled host is the direct source of infection of another host in a pathogen gene genealogy. These methods enable forensic applications, such as source-case attribution, for infectious disease epidemics with incomplete sampling, which is usually the case for high-morbidity community-acquired pathogens like HIV, Influenza and Dengue virus. These methods also enable epidemiological applications such as the identification of factors that increase the risk of transmission. We demonstrate these methods in the context of the HIV epidemic in Detroit, Michigan, and we evaluate the suitability of current sequence databases for forensic and epidemiological investigations. We find that currently available sequences collected for drug resistance testing of HIV are unlikely to be useful in most forensic investigations, but are useful for identifying transmission risk factors.
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