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laduplessis committed Jul 24, 2017
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4 changes: 2 additions & 2 deletions README.md
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<figure>
<a id="fig:hostsets"></a>
<img src="figures/FMDV_transmission_direct_transmissions.jpg" alt="">
<figcaption>Figure 9: The direct transmission network using `Make_transmission_tree_alternative.py`.</figcaption>
<figcaption>Figure 9: The direct transmission network using graphviz (Make_transmission_tree_alternative.py).</figcaption>
</figure>
<br>


<figure>
<a id="fig:hostsets"></a>
<img src="figures/FMDV3_out_direct_transmissions.png" alt="">
<figcaption>Figure 10: The direct transmission network using `Make_transmission_tree.py`.</figcaption>
<figcaption>Figure 10: The direct transmission network using graph-tool (Make_transmission_tree.py).</figcaption>
</figure>
<br>

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75 changes: 47 additions & 28 deletions main.tex
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\vspace{4mm}

% Enter the names of all the authors here
{\Large {\em Louis du Plessis}}
{\Large {\em Louis du Plessis and Nicola de Maio}}
\end{center}

Transmission tree reconstruction with the structured coalescent
Expand Down Expand Up @@ -151,6 +151,8 @@ \subsubsection{Python}\label{python}
better looking figure.

Python should already be installed on most Mac OS X or Linux systems.
Both graphviz and graph-tool are available on Homebrew, and graphviz can
also be installed using \lstinline!apt-get! on Linux systems.

\clearpage

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contains 11 viral sequences from 10 farms. Four sequences were sampled
during the first cluster and a further 7 during the second cluster. In
addition, we also have the earliest and latest possible dates during
which each farm was infected with the disease (Figure
\ref{fig:outbreak}). The data were first analysed in
\citep{Cottam2008PlosPath} and later reanalysed using SCOTTI in
\citep{deMaio2016}.
which each farm was infected with the disease, informed by the culling
time and first appearance of symptoms (Figure \ref{fig:outbreak}). The
data were first analysed in \citep{Cottam2008PlosPath} and later
reanalysed using SCOTTI in \citep{deMaio2016}.

\begin{figure}
\centering
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This parameter estimates the number of hosts in the outbreak. It is
bounded below by 10 (the number of sampled farms) and above by 20, which
was our input upper bound. We see that there appears to be significant
support for almost twice as many farms to have been infected as farms
where diseased animals were found. The parameter
\textbf{migModel.popSize} logs the effective population size of the
hosts, which is directly proportional to the genetic diversity of the
virus within each farm. We see that the effective population size is
quite low, which is probably due to the relatively short period of the
outbreak. The equal infection rate between farms is logged by
\textbf{migModel.rateMig} and the clock rate by \textbf{mutationRate}.
If the clock rate seems low for a virus, keep in mind that we measure
time in days. Thus, the units for substitutions are in
substitutions/site/day instead of the usual substitutions/site/year.
was our input upper bound. However, it is difficult to interpret this
parameter as the number of farms that were infected, because (i) one
non-sampled deme may actually represent a series of hosts with no time
overlap, or a single host with unlimited exposure time (like
environmental contamination); (ii) non-sampled hosts are not necessarily
infected; (iii) many non-sampled hosts might just represent one host
with large population size, like an environmental endemic source.

The parameter \textbf{migModel.popSize} logs the effective population
size of the hosts, which is directly proportional to the genetic
diversity of the virus within each farm. We see that the effective
population size is quite low, which is probably due to the relatively
short period of the outbreak. The equal infection rate between farms is
logged by \textbf{migModel.rateMig} and the clock rate by
\textbf{mutationRate}. If the clock rate seems low for a virus, keep in
mind that we measure time in days. Thus, the units for substitutions are
in substitutions/site/day instead of the usual substitutions/site/year.

\begin{framed}
Select \textbf{tree.height} (Figure \ref{fig:tmrca})
Expand All @@ -542,11 +549,12 @@ \subsection{Analysing the output in Tracer and
\end{figure}

This parameter is the TMRCA of the sequences included in the
transmission tree. The median estimate is 67.36 days. Since the most
recent sample was taken on day 62 of the outbreak (IP8-62), this
indicates that it is unlikely that the outbreak started more than a week
before it was discovered and confirmed, which is an encouraging sign for
the surveillance of foot and mouth disease in the United Kingdom.
transmission tree. The median estimate is 67.36 days, with a 95\% HPD
between 60.76 and 78.55 days. Since the most recent sample was taken on
day 62 of the outbreak (IP8-62), this indicates that it is unlikely that
the outbreak started more than a week before it was discovered and
confirmed, which is an encouraging sign for the surveillance of foot and
mouth disease in the United Kingdom.

\begin{framed}
Open \textbf{TreeAnnotator} and set the \textbf{Burnin percentage} to
Expand All @@ -570,9 +578,13 @@ \subsection{Analysing the output in Tracer and
\textbf{Colour by} dropdown box select \textbf{host}. Check
\textbf{Gradient} and increase the \textbf{Line Weight} to \textbf{5}.

Increase the \textbf{Font Size} under \textbf{Tip Labels}. Check
\textbf{Node Labels} and select \textbf{host.prob} from the dropdown box
and increase the \textbf{Font size}.
Increase the \textbf{Font Size} under \textbf{Tip Labels} and change
\textbf{Display} to \textbf{host}. Check \textbf{Node Labels} and select
\textbf{host.prob} from the dropdown box and increase the \textbf{Font
size}.

Under \textbf{Node Shape} select \textbf{Circle} and set the size by
\textbf{host.prob} and the colour by \textbf{host}.

Check \textbf{Legend} and select \textbf{host} as \textbf{Attribute}.
\end{framed}
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farms. We will use the script
\lstinline!Make_transmission_tree_alternative.py!, which uses the
\lstinline!graphviz! package. If this package is not installed you will
have to install it, either through homebrew or by installing the
have to install it, either through Homebrew or by installing the
Anaconda Python distribution. Alternatively, if you have previously
installed the \lstinline!graph-tool! package you may use the script
\lstinline!Make_transmission_tree.py!, which will produce better looking
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\begin{figure}
\centering
\includegraphics[max width=\textwidth, max height=0.9\textheight]{figures/FMDV_transmission_direct_transmissions.jpg}
\caption{The direct transmission network.}
\caption{The direct transmission network using graphviz.}
\label{fig:hostsets}
\end{figure}

\begin{figure}
\centering
\includegraphics[max width=\textwidth, max height=0.9\textheight]{figures/FMDV3_out_direct_transmissions.png}
\caption{The direct transmission network using graph-tool.}
\label{fig:hostsets}
\end{figure}

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% Please do not change the license
% Add the author names and relevant links
% Add any other aknowledgments here
\href{http://creativecommons.org/licenses/by/4.0/}{\includegraphics[scale=0.8]{figures/ccby.pdf}} This tutorial was written by Louis du Plessis for \href{https://taming-the-beast.github.io}{Taming the BEAST} and is licensed under a \href{http://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International License}.
\href{http://creativecommons.org/licenses/by/4.0/}{\includegraphics[scale=0.8]{figures/ccby.pdf}} This tutorial was written by Louis du Plessis and Nicola de Maio for \href{https://taming-the-beast.github.io}{Taming the BEAST} and is licensed under a \href{http://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International License}.


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