Imperial College London
Alternate

DrOliverRatmann

Faculty of Natural Sciences, Department of Mathematics

Reader in Statistics and Machine Learning for Public Good
 
 
 
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Contact

 

oliver.ratmann05 Website

 
 
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Location

 

525Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chatzilena:2019:10.1016/j.epidem.2019.100367,
author = {Chatzilena, A and van, Leeuwen E and Ratmann, O and Baguelin, M and Demiris, N},
doi = {10.1016/j.epidem.2019.100367},
journal = {Epidemics: the journal of infectious disease dynamics},
title = {Contemporary statistical inference for infectious disease models using Stan},
url = {http://dx.doi.org/10.1016/j.epidem.2019.100367},
volume = {29},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper is concerned with the application of recent statistical advances to inference of infectious disease dynamics. We describe the fitting of a class of epidemic models using Hamiltonian Monte Carlo and variational inference as implemented in the freely available Stan software. We apply the two methods to real data from outbreaks as well as routinely collected observations. Our results suggest that both inference methods are computationally feasible in this context, and show a trade-off between statistical efficiency versus computational speed. The latter appears particularly relevant for real-time applications.
AU  - Chatzilena,A
AU  - van,Leeuwen E
AU  - Ratmann,O
AU  - Baguelin,M
AU  - Demiris,N
DO  - 10.1016/j.epidem.2019.100367
PY  - 2019///
SN  - 1755-4365
TI  - Contemporary statistical inference for infectious disease models using Stan
T2  - Epidemics: the journal of infectious disease dynamics
UR  - http://dx.doi.org/10.1016/j.epidem.2019.100367
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31591003
UR  - http://hdl.handle.net/10044/1/74089
VL  - 29
ER  -
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