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Add repo citation.

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evogytis committed Dec 12, 2017
1 parent b1fe9ab commit 111da36d847ce8933514f82eebc0aef80e7c5ecd
Showing with 18 additions and 2 deletions.
  1. +17 −1 mers-structure.bib
  2. +1 −1 mers-structure.tex
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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/
%% Created for Gytis Dudas at 2017-11-22 13:43:26 -0800
%% Created for Gytis Dudas at 2017-12-12 15:37:19 -0800
%% Saved with string encoding Unicode (UTF-8)
@misc{mers-structure,
Author = {Dudas, Gytis},
Copyright = {CC-BY-SA-4.0},
Date-Added = {2017-12-12 23:29:20 +0000},
Date-Modified = {2017-12-12 23:37:19 +0000},
File = {Snapshot:/Users/evogytis/Zotero/storage/H7FY5DXN/mers-structure.html:text/html},
Howpublished = {b1fe9abbd633222342f7850ec01a494812e2ca9b},
Month = aug,
Publisher = {Bedford Lab},
Shorttitle = {mers-structure},
Title = {mers-structure: {Looking} into {MERS}-{CoV} dynamics through the structured coalescent lens},
Url = {https://github.com/blab/mers-structure},
Urldate = {2017-12-12},
Year = {2017},
Bdsk-Url-1 = {https://github.com/blab/mers-structure}}
@article{rasmussen_phylodynamic_2014,
Abstract = {Author Summary Mathematical models play an important role in our understanding of what processes drive the complex population dynamics of infectious pathogens. Yet developing statistical methods for fitting models to epidemiological data is difficult. Epidemiological data is often noisy, incomplete, aggregated across different scales and generally provides only a partial picture of the underlying disease dynamics. Using nontraditional sources of data, like molecular sequences of pathogens, can provide additional information about epidemiological dynamics. But current ``phylodynamic'' inference methods for fitting models to genealogies reconstructed from sequence data have a number of major limitations. We present a statistical framework that builds upon earlier work to address two of these limitations: population structure and stochasticity. By incorporating population structure, our framework can be applied in cases where the host population is divided into different subpopulations, such as by spatial isolation. Our framework also takes into consideration stochastic noise and can therefore capture the inherent variability of epidemiological dynamics. These advances allow for a much wider class of epidemiological models to be fit to genealogies in order to estimate key epidemiological parameters and to reconstruct past disease dynamics.},
Author = {Rasmussen, David A. and Volz, Erik M. and Koelle, Katia},
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@@ -713,7 +713,7 @@ \subsection*{Demographic inference of MERS-CoV in the reservoir}
\subsection*{Data availability}
Sequence data and all analytical code is publicly available at \href{https://github.com/blab/structured-mers}{github.com/blab/structured-mers}.
Sequence data and all analytical code is publicly available at \href{https://github.com/blab/structured-mers}{github.com/blab/structured-mers} \citep{mers-structure}.
\section*{Acknowledgements}
We would like to thank Allison Black for useful discussion and advice.

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