Imperial College London
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Dr Kris V Parag

Faculty of Medicine, School of Public Health

Research Fellow
 
 
 
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Contact

 

k.parag

 
 
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Location

 

Wright Fleming WingSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Parag:2020:sysbio/syaa035,
author = {Parag, K and Donnelly, C},
doi = {sysbio/syaa035},
journal = {Systematic Biology},
pages = {1163--1179},
title = {Adaptive estimation for epidemic renewal and phylogenetic skyline models},
url = {http://dx.doi.org/10.1093/sysbio/syaa035},
volume = {69},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of observed incident cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimizing p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimizes p so that R and N estimates properly and meaningfully adapt to available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems, given minimal knowledge of the parameter space, and exposes statistical similarities among renewal, skyline, and other models in biology. Rigorous and interpretable model selection is necessary if trustworthy and just
AU  - Parag,K
AU  - Donnelly,C
DO  - sysbio/syaa035
EP  - 1179
PY  - 2020///
SN  - 1063-5157
SP  - 1163
TI  - Adaptive estimation for epidemic renewal and phylogenetic skyline models
T2  - Systematic Biology
UR  - http://dx.doi.org/10.1093/sysbio/syaa035
UR  - http://hdl.handle.net/10044/1/79245
VL  - 69
ER  -
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