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evogytis committed Nov 22, 2017
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  1. +22 −1 mers-structure.bib
  2. +3 −4 mers-structure.tex
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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/
%% Created for Trevor Bedford at 2017-11-21 17:08:05 -0800
%% Created for Gytis Dudas at 2017-11-22 13:43:26 -0800
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@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},
Date-Added = {2017-11-22 21:43:21 +0000},
Date-Modified = {2017-11-22 21:43:21 +0000},
Doi = {10.1371/journal.pcbi.1003570},
File = {Full Text PDF:/Users/evogytis/Zotero/storage/2RVD7HVU/Rasmussen et al. - 2014 - Phylodynamic Inference for Structured Epidemiologi.pdf:application/pdf;Snapshot:/Users/evogytis/Zotero/storage/B7LBIVJT/article.html:text/html},
Issn = {1553-7358},
Journal = {PLOS Computational Biology},
Keywords = {Epidemiology, Algorithms, HIV, HIV epidemiology, Infectious disease epidemiology, Pathogens, Population density, Population dynamics},
Month = apr,
Number = {4},
Pages = {e1003570},
Title = {Phylodynamic {Inference} for {Structured} {Epidemiological} {Models}},
Url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003570},
Urldate = {2017-11-22},
Volume = {10},
Year = {2014},
Bdsk-Url-1 = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003570},
Bdsk-Url-2 = {http://dx.doi.org/10.1371/journal.pcbi.1003570}}
@article{frost_viral_2010,
Author = {Frost, Simon DW and Volz, Erik M},
Date-Added = {2017-11-22 01:07:16 +0000},
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@@ -389,15 +389,14 @@ \subsection*{MERS-CoV epidemiology}
We also looked at potential seasonality in MERS-CoV spillover into humans.
Our analyses indicated a period of three months where the odds of a sequenced spillover event are increased, with timing consistent with an enzootic amongst camel calves (Figure \ref{seasonality}).
As a result of our identification of large and asymmetric flow of viral lineages into humans we also find that the basic reproduction number for MERS-CoV in humans is well below the epidemic threshold (Figure \ref{mers_epi}).
Having said that, structured population models explicitly relating epidemiological parameters to the branching process observed in sequence data \citep{kuhnert_phylodynamics_2016} should ideally be used in cases like MERS-CoV, but in our case lack of good prior information and MCMC convergence issues prevented us from employing such models here.
Having said that, there are highly customisable coalescent methods available that extend the methods used here to accommodate migration rates and population sizes varying through time, and integrate alternative sources of information that are able to fit stochastic noisy nonlinear models \citep{rasmussen_phylodynamic_2014} which would be more appropriate for MERS-CoV.
Some distinct aspects of MERS-CoV epidemiology could not be captured in our methodology, such as hospital outbreaks where $R_{0}$ is expected to be consistently closer to 1.0 compared to community transmission of MERS-CoV.
Outside of coalescent-based models there are population structure models that explicitly relate epidemiological parameters to the branching process observed in sequence data \citep{kuhnert_phylodynamics_2016}, but often rely on specifying numerous informative priors and can suffer from MCMC convergence issues.
Strong population structure in viruses often arises through limited gene flow, either due to geography \citep{dudas_virus_2017}, ecology \citep{smith_dating_2009} or evolutionary forces \citep{turner_genomic_2005,dudas_reassortment_2015}.
On a smaller scale population structure can unveil important details about transmission patterns, such as identifying reservoirs and understanding spillover trends and risk, much as we have done here.
There is much room for improvement, however.
The population structure model applied in this study does not have an implementation allowing for changes in effective population size through time.
When properly understood naturally arising barriers to gene flow can be exploited for more efficient disease control and prevention, as well as risk management.
\subsection*{Transmissibility differences between zoonoses and pandemics}
Severe acute respiratory syndrome (SARS) coronavirus, a Betacoronavirus like MERS-CoV, caused a serious epidemic in humans in 2003, with over 8000 cases and nearly 800 deaths.
Since MERS-CoV was also able to cause significant pathogenicity in the human host it was inevitable that parallels would be drawn between MERS-CoV and SARS-CoV at the time of MERS discovery in 2012.

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