ArticleCell systems2022
SOPHIE: Viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework.
Article in Cell systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Network Models for HIV Spread.Journal of acquired immune deficiency syndromes (1999) · 2025Pooled it
- The tree labeling polytope: A unified approach to ancestral reconstruction problems.Cell systems · 2026Article
- A graph homomorphism approach for unraveling histories of metastatic cancers and viral outbreaks under evolutionary constraints.Nature communications · 2025Article
- JUNIPER: Reconstructing Transmission Events from Next-Generation Sequencing Data at Scale.Research square · 2025Article
- JUNIPER: Reconstructing Transmission Events from Next-Generation Sequencing Data at Scale.medRxiv : the preprint server for health sciences · 2025Article
- Bayesian estimation of transmission networks for infectious diseases.Journal of mathematical biology · 2025Article
- Inferring Viral Transmission Pathways from Within-Host Variation.medRxiv : the preprint server for health sciences · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed for phylogenetic inference of transmission histories, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, although common source outbreaks violate this assumption. We propose a maximum likelihood framework, SOPHIE, based on the integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modeled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity, and accurately infers transmissions without case-specific epidemiological data.
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Registered trials
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