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cpjewell
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Added documentation .tex file. Needs to be built before distributing the package. Updated the README file to reflect this. Implemented in top level Makefile.am.
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Makefile.am

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@@ -6,12 +6,12 @@ DISTCHECK_CONFIGURE_FLAGS="CXXFLAGS=-O3 -fomit-frame-pointer -DNDEBUG -DBOOST_MP
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exampledir = $(pkgdatadir)/examples
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example_DATA = $(top_srcdir)/auseiConfig.xml
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EXTRA_DIST = $(top_srcdir)/auseiConfig.xml
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EXTRA_DIST = $(top_srcdir)/auseiConfig.xml $(top_srcdir)/doc/model.pdf
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dist-hook:
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chmod u+w $(distdir)/README
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sed -i 's/%PACKAGE%/@PACKAGE@/g' $(distdir)/README
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sed -i 's/%VERSION%/@VERSION@/g' $(distdir)/README
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chmod u-w $(distdir)/README
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docdir = $(datadir)/doc/@PACKAGE@
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doc_DATA = $(top_srcdir)/README
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doc_DATA = $(top_srcdir)/README $(top_srcdir)/doc/model.pdf

README

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@@ -8,14 +8,15 @@ The code contained in this repository runs alongside research conducted between
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The software is distributed under the GPLv3 license, allowing users to modify and redistribute the source code.
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It is provided WITHOUT WARRANTY and is intended for academic research purposes.
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Copyright Chris Jewell <[email protected]> 2012
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Copyright Chris Jewell <[email protected]> 2014
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See $(PREFIX)/doc/%PACKAGE%/model.pdf for technical model details.
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System requirements
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====================
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Boost >= 1.51.0 with program_options and boost_mpi compiled in.
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MPI library compatible with Boost::MPI >= 1.51.0
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Boost >= 1.51.0 with boost_program_options, boost_serialization and boost_mpi compiled in.
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GNU Scientific Library >= 1.15
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Compiling
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==============================
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To point the build process at non-standard library locations
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(e.g. for GSL and Boost), use the CXXFLAGS and LDFLAGS environment
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variables. For example:
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(e.g. for GSL and Boost), use the --with-gsl-prefix and --with-boost
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options to configure. For example:
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$ ./configure --with-gsl-prefix=/usr/local/packages/gsl-1.15 --with-boost=/usr/local/packages/boost_1_51_0
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$ ./configure CXXFLAGS="-I/usr/local/packages/gsl-1.15/include -I/usr/local/packages/boost_1_51_0/include" \
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LDFLAGS="-L/usr/local/packages/gsl-1.15/lib -L/usr/local/packages/boost_1_51_0/lib"
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See ./configure --help for further options for selecting GSL and Boost locations.
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Running %PACKAGE%
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an example config file which you can edit. Change the values in this file, BUT DO NOT ALTER THE
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STRUCTURE OF THE XML.
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The output files are named according to the output file prefix, with .parms and .occ appended. This
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version of %PACKAGE% writes nothing to the .occ file, and the posterior draws for the parameters are
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contained in the .parms file. The latter is CSV format, with the names of the parameters as column headings.
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Each row then contains a sample of the chain for each iteration.
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Since auseiMcmc is built using MPI, you can run it under 'mpirun' for parallel execution. The MCMC
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algorithm requires /fast/ interconnects, so it is recommended to either run the package on a large
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multicore machine, or on a specialised distributed HPC system. For example:
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$ mpirun -np 128 auseiMcmc -c myConf.xml
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Population file format
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======================
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doc/model.tex

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\documentclass[english]{article}
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\usepackage[T1]{fontenc}
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\usepackage[latin9]{inputenc}
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\usepackage{geometry}
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\geometry{verbose,tmargin=2cm,bmargin=2cm,lmargin=2cm,rmargin=2cm}
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\setlength{\parskip}{\medskipamount}
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\setlength{\parindent}{0pt}
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\usepackage{bm}
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\usepackage{amsmath}
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\usepackage{babel}
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\begin{document}
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\title{Australian Equine Influenza model}
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\maketitle
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\section{infer-ausei version 4.0}
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The following documentation describes the model used in infer-ausei
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version 4.0. Please refer to the \texttt{README} file for details
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of how to make inference on this model.
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\subsection{Between-property model}
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The state of properties during the epidemic is modelled as a continuous
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time SIO (Susceptible, Infected, Onset) process, with pairwise transmission
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rate
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\[
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\beta_{ij}(t)=\mu\cdot h_{i}(t-I_{i})n_{i}a_{i}^{\xi}\cdot n_{j}a_{j}^{\zeta}\theta^{\bm{1}[j\in\mathcal{V}(t)]}\frac{\delta}{\delta^{2}+\rho_{ij}^{2}}\hspace{1em}i\in\mathcal{I},j\in\mathcal{S}
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\]
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and assumes a fixed 2 day time from infection to onset \emph{at the
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between property level}. $\mu$ is the baseline transmission rate,
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$n_{k}$ is the number of horses on property $k$, $a_{k}$ is the
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area of property $k$ with non-linear parameters $\xi$ and $\zeta$
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giving the effect (note that if these non-linear parameters were 0,
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this would imply no effect of area). $\theta$ is the effect of vaccination
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on the susceptible properties with $\mathcal{V}(t)$ the set of vaccinated
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properties at time $t$. $\rho_{ij}$ is the Euclidean distances (map-units/1000)
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between centroids(?) of the properties $i$ and $j$, and $\delta$
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is the decay parameter for our Cauchy distance kernel. The function
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$h_{i}(t-I_{i})$ is the infectivity of property $i$, taken to be
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proportional to the number of animals infected according to the within-property
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model described below.
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At time $t$, the \emph{infectious pressure} $\lambda_{j}(t)$ on
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property $j$ is
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\[
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\lambda_{j}(t)=\epsilon_{t}+\sum_{i\in}\beta_{ij}(t)
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\]
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where
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\[
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\epsilon_{t}=\begin{cases}
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\epsilon_{0} & \mbox{ if }t<10\\
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\epsilon_{10} & \mbox{if }10\leq t<44\\
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\epsilon_{44} & \mbox{if }44\leq t
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\end{cases}
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\]
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corresponding to control events at $t=10$ and $t=44$ (remind me
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what these events were?).
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\subsection{Within-property model}
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At the \emph{within property level}, $h_{i}(s)=I_{i}(s)/n_{i}$ function
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returns the proportion of horses infected on property $i$ at time
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$s$ after $i$'s infection time, where $I_{i}(s)$ is the solution
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to a deterministic SEIR model
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\begin{eqnarray*}
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\frac{dS_{i}(t)}{dt} & = & -\beta\frac{S_{i}(t)I_{i}(t)}{n_{i}}\\
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\frac{dE_{i}(t)}{dt} & = & \beta\frac{S_{i}(t)I_{i}(t)}{n_{i}}-\omega E_{i}(t)\\
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\frac{dI_{i}(t)}{dt} & = & \omega E_{i}(t)-\nu I_{i}(t)\\
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\frac{dR_{i}(t)}{dt} & = & \nu I_{i}(t)
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\end{eqnarray*}
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where $\omega=$1 and $\nu=1/6$. We set $h(s)$=0 for $I_{i}(s)<0.5$
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horses.
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Note that a property ``runs out'' of infectiousness as all the horses
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recover. This is why the SIO model above does not have an ``R''
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compartment -- ``O'' stage properties simply stop being infectious
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after a while.
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\end{document}

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