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evogytis committed Nov 9, 2017
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%% This BibTeX bibliography file was created using BibDesk.
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%% Created for Gytis Dudas at 2017-11-09 12:11:15 -0800
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Abstract = {When viruses spread, outbreaks can be spawned in previously unaffected regions. Depending on the time and mode of introduction, each regional outbreak can have its own epidemic dynamics. The migration and phylodynamic processes are often intertwined and need to be taken into account when analyzing temporally and spatially structured virus data. In this article, we present a fully probabilistic approach for the joint reconstruction of phylodynamic history in structured populations (such as geographic structure) based on a multitype birth--death process. This approach can be used to quantify the spread of a pathogen in a structured population. Changes in epidemic dynamics through time within subpopulations are incorporated through piecewise constant changes in transmission parameters.We analyze a global human influenza H3N2 virus data set from a geographically structured host population to demonstrate how seasonal dynamics can be inferred simultaneously with the phylogeny and migration process. Our results suggest that the main migration path among the northern, tropical, and southern region represented in the sample analyzed here is the one leading from the tropics to the northern region. Furthermore, the time-dependent transmission dynamics between and within two HIV risk groups, heterosexuals and injecting drug users, in the Latvian HIV epidemic are investigated. Our analyses confirm that the Latvian HIV epidemic peaking around 2001 was mainly driven by the injecting drug user risk group.},
Author = {K{\"u}hnert, Denise and Stadler, Tanja and Vaughan, Timothy G. and Drummond, Alexei J.},
Date-Added = {2017-11-09 20:11:12 +0000},
Date-Modified = {2017-11-09 20:11:12 +0000},
Doi = {10.1093/molbev/msw064},
File = {Full Text PDF:/Users/evogytis/Library/Application Support/Zotero/Profiles/1sfxzhn6.default/zotero/storage/3VVUNG63/K{\"u}hnert et al. - 2016 - Phylodynamics with Migration A Computational Fram.pdf:application/pdf;Snapshot:/Users/evogytis/Library/Application Support/Zotero/Profiles/1sfxzhn6.default/zotero/storage/HNRP2ZWM/2578541.html:text/html},
Issn = {0737-4038},
Journal = {Molecular Biology and Evolution},
Month = aug,
Number = {8},
Pages = {2102--2116},
Shorttitle = {Phylodynamics with {Migration}},
Title = {Phylodynamics with {Migration}: {A} {Computational} {Framework} to {Quantify} {Population} {Structure} from {Genomic} {Data}},
Url = {},
Urldate = {2017-11-09},
Volume = {33},
Year = {2016},
Bdsk-Url-1 = {},
Bdsk-Url-2 = {}}
Abstract = {The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum-likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbc L sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page:},
Author = {Guindon, St{\'e}phane and Gascuel, Olivier and Rannala, Bruce},
@@ -391,11 +391,15 @@ \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.
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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