ArticlePLoS computational biology2012
Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Epidemiological inference for emerging viruses using segregating sites.Nature communications · 2023Article
- The potential of genomics for infectious disease forecasting.Nature microbiology · 2022Review
- 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
- Phylodynamic Model Adequacy Using Posterior Predictive Simulations.Systematic biology · 2019Article
- Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art.Journal of the Royal Society, Interface · 2019Article
- Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python.Epidemics · 2018Article
- Evidence Synthesis for Stochastic Epidemic Models.Statistical science : a review journal of the Institute of Mathematical Statistics · 2018Article
- Identifying spatio-temporal dynamics of Ebola in Sierra Leone using virus genomes.Journal of the Royal Society, Interface · 2017Article
- Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.PLoS computational biology · 2017Article
- Fundamentals and Recent Developments in Approximate Bayesian Computation.Systematic biology · 2017Article
- Capturing the dynamics of pathogens with many strains.Journal of mathematical biology · 2016Review
- Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases.Current epidemiology reports · 2016Review
- Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.Molecular biology and evolution · 2015Article
- The Role of Human Transportation Networks in Mediating the Genetic Structure of Seasonal Influenza in the United States.PLoS pathogens · 2015Article
- Modeling infectious disease dynamics in the complex landscape of global health.Science (New York, N.Y.) · 2015Review
- Article
- Two-phase importance sampling for inference about transmission trees.Proceedings. Biological sciences · 2014Article
- Phylogenetic studies of transmission dynamics in generalized HIV epidemics: an essential tool where the burden is greatest?Journal of acquired immune deficiency syndromes (1999) · 2014Review
- Approximate Bayesian inference for complex ecosystems.F1000prime reports · 2014Review
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5 authors.
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Abstract
A key priority in infectious disease research is to understand the ecological and evolutionary drivers of viral diseases from data on disease incidence as well as viral genetic and antigenic variation. We propose using a simulation-based, Bayesian method known as Approximate Bayesian Computation (ABC) to fit and assess phylodynamic models that simulate pathogen evolution and ecology against summaries of these data. We illustrate the versatility of the method by analyzing two spatial models describing the phylodynamics of interpandemic human influenza virus subtype A(H3N2). The first model captures antigenic drift phenomenologically with continuously waning immunity, and the second epochal evolution model describes the replacement of major, relatively long-lived antigenic clusters. Combining features of long-term surveillance data from The Netherlands with features of influenza A (H3N2) hemagglutinin gene sequences sampled in northern Europe, key phylodynamic parameters can be estimated with ABC. Goodness-of-fit analyses reveal that the irregularity in interannual incidence and H3N2's ladder-like hemagglutinin phylogeny are quantitatively only reproduced under the epochal evolution model within a spatial context. However, the concomitant incidence dynamics result in a very large reproductive number and are not consistent with empirical estimates of H3N2's population level attack rate. These results demonstrate that the interactions between the evolutionary and ecological processes impose multiple quantitative constraints on the phylodynamic trajectories of influenza A(H3N2), so that sequence and surveillance data can be used synergistically. ABC, one of several data synthesis approaches, can easily interface a broad class of phylodynamic models with various types of data but requires careful calibration of the summaries and tolerance parameters.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.