Estimating pathogen spread using structured coalescent and birth–death models: A quantitative comparison
Epidemics·
- DOI
- 10.1016/j.epidem.2024.100795
- PMID
- 39461051
- PMCID
- —
- OpenAlex
- W4403541477
- Study type
- Genomic study
- Publisher
- Elsevier BV
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The work offers modelling guidance for infectious disease analysts: either model is suitable for endemic settings, whereas constant-size structured coalescent models should be avoided for epidemic outbreaks or changing population sizes, in favour of variable-size coalescent or birth–death approaches.
Structured evidence summary
Research question
The study examines how the choice between structured coalescent and multitype birth–death phylodynamic models affects the accuracy of migration rate and source-location inference across simulated endemic and epidemic scenarios.
Study design
A simulation study comparing inferential performance of the structured coalescent model (constant population size) and the multitype birth–death model (constant rate) across a range of migration rates, evaluated under endemic and epidemic outbreak conditions.
Population and setting
Simulated pathogen phylogenies representing disease spread between subpopulations in endemic settings and epidemic outbreaks; no specific geography, host species, or real-world cohort is described.
Main findings
In epidemic outbreaks, the birth–death model recovered migration rates more accurately than the constant-size coalescent model across the migration rates tested. In endemic scenarios, the two models produced comparable coverage and accuracy of migration rates, with the coalescent model yielding more precise estimates. Both models estimated source location similarly regardless of scenario.
Public-health relevance
The work offers modelling guidance for infectious disease analysts: either model is suitable for endemic settings, whereas constant-size structured coalescent models should be avoided for epidemic outbreaks or changing population sizes, in favour of variable-size coalescent or birth–death approaches.
Important limitations
The summary is limited to the supplied single-article abstract and bibliographic metadata; the original paper is required for decision-grade interpretation. Explicit limitations are not stated in the supplied abstract, and no specific geographic, host, or pathogen cohort details are provided beyond a general association with COVID-19 in the classifier metadata.
GIDS interpretation
The article is most relevant for users selecting structured phylodynamic methods to characterise pathogen spread between subpopulations. It does not constitute real-time surveillance evidence and should be retrieved through GIDS by users focused on genomic epidemiology, transmission dynamics, or outbreak-investigation methodology rather than as a confirmation of any active outbreak signal.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 8 auditable classifier relationships to diseases, places, topics, and study design.