Imperial College London
Alternate

DrXavierDidelot

Faculty of Medicine, School of Public Health

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 7594 3622x.didelot

 
 
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Location

 

G30Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Didelot:2021:10.1101/2021.01.18.427056,
author = {Didelot, X and Geidelberg, L and COVID-19, Genomics UK COG-UK consortium and Volz, EM},
doi = {10.1101/2021.01.18.427056},
journal = {bioRxiv},
title = {Model design for non-parametric phylodynamic inference and applications to pathogen surveillance.},
url = {http://dx.doi.org/10.1101/2021.01.18.427056},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Inference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. The combination of non-parametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments, and apply the methodology to reconstruct the previously described waves in the seventh pandemic of cholera. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.
AU  - Didelot,X
AU  - Geidelberg,L
AU  - COVID-19,Genomics UK COG-UK consortium
AU  - Volz,EM
DO  - 10.1101/2021.01.18.427056
PY  - 2021///
TI  - Model design for non-parametric phylodynamic inference and applications to pathogen surveillance.
T2  - bioRxiv
UR  - http://dx.doi.org/10.1101/2021.01.18.427056
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34426812
ER  -
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