ArticleMolecular biology and evolution2017
Quantifying Transmission Heterogeneity Using Both Pathogen Phylogenies and Incidence Time Series.
Article in Molecular biology and evolution, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Extensive cryptic circulation sustains mpox among men who have sex with men.Nature communications · 2026Article
- Inference of Multiple Mergers while Dating a Pathogen Phylogeny.Systematic biology · 2025Article
- Evolution and transmission dynamics of wild poliovirus in Pakistan and Afghanistan (2012-2023).Nature communications · 2025Article
- Estimating epidemic dynamics with genomic and time series data.Journal of the Royal Society, Interface · 2025Article
- Multiple merger coalescent inference of effective population size.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering.PLoS computational biology · 2024Article
- Molecular epidemiology of the HIV-1 epidemic in Fiji.Npj viruses · 2024Article
- Epidemiological inference for emerging viruses using segregating sites.Nature communications · 2023Article
- Monitoring real-time transmission heterogeneity from incidence data.PLoS computational biology · 2022Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- Markov genealogy processes.Theoretical population biology · 2022Article
- A computationally tractable birth-death model that combines phylogenetic and epidemiological data.PLoS computational biology · 2022Article
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Quantifying superspreading for COVID-19 using Poisson mixture distributions.Scientific reports · 2021Article
- Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK.Science (New York, N.Y.) · 2021Article
- Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador.Virus evolution · 2021Article
- Genomic epidemiology of a densely sampled COVID-19 outbreak in China.Virus evolution · 2021Article
- Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations.Scientific reports · 2020Article
- Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase.Nature communications · 2020Article
- Inferring transmission heterogeneity using virus genealogies: Estimation and targeted prevention.PLoS computational biology · 2020Article
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3 authors.
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Abstract
Heterogeneity in individual-level transmissibility can be quantified by the dispersion parameter k of the offspring distribution. Quantifying heterogeneity is important as it affects other parameter estimates, it modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. Aggregated data such as incidence time series are often not sufficiently informative to estimate k. Incorporating phylogenetic analysis can help to estimate k concurrently with other epidemiological parameters. We have developed an inference framework that uses particle Markov Chain Monte Carlo to estimate k and other epidemiological parameters using both incidence time series and the pathogen phylogeny. Using the framework to fit a modified compartmental transmission model that includes the parameter k to simulated data, we found that more accurate and less biased estimates of the reproductive number were obtained by combining epidemiological and phylogenetic analyses. However, k was most accurately estimated using pathogen phylogeny alone. Accurately estimating k was necessary for unbiased estimates of the reproductive number, but it did not affect the accuracy of reporting probability and epidemic start date estimates. We further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. Finally, we used the inference framework to estimate transmission parameters from epidemiological and genetic data collected during a poliovirus outbreak. Despite the large degree of phylogenetic uncertainty, we demonstrated that incorporating phylogenetic data in parameter inference improved the accuracy and precision of estimates.
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