The UFZ services GitLab and Mattermost will be unavailable on Monday, July 4 from 06:00 AM to 08:00 AM due to maintenance work.

Commit b5bfbc65 authored by Adam Reichold's avatar Adam Reichold
Browse files

Another talk on (the lack of) modelling progress.

parent 1d4780ff
Pipeline #64008 passed with stage
in 3 minutes and 23 seconds
documents/dach21/*.jpg filter=lfs diff=lfs merge=lfs -text
documents/dach21/*.png filter=lfs diff=lfs merge=lfs -text
documents/dach21/*.mp4 filter=lfs diff=lfs merge=lfs -text
documents/eunis/*.png filter=lfs diff=lfs merge=lfs -text
documents/modelling_progress_3/*.png filter=lfs diff=lfs merge=lfs -text
documents/modelling_progress_3/*.pdf filter=lfs diff=lfs merge=lfs -text
......@@ -74,6 +74,7 @@ document:
- documents/modelling_concepts/modelling_concepts.pdf
- documents/modelling_progress_1/modelling_progress.pdf
- documents/modelling_progress_2/modelling_progress.pdf
- documents/modelling_progress_3/modelling_progress.pdf
- documents/mistakes_made/mistakes_made.pdf
- documents/parallel_simulation/parallel_simulation.pdf
- documents/sampling_effects/sampling_effects.pdf
......
......@@ -5,7 +5,7 @@ RUN export DEBIAN_FRONTEND=noninteractive && \
apt-get install --yes --no-install-recommends \
curl pkg-config \
libopenmpi-dev llvm clang mingw-w64 \
texlive texlive-luatex texlive-latex-extra texlive-fonts-extra texlive-bibtex-extra biber texlive-lang-german \
texlive texlive-luatex texlive-latex-extra texlive-fonts-extra texlive-bibtex-extra biber texlive-lang-german fonts-noto-color-emoji \
python3-matplotlib python3-pandas && \
luaotfload-tool --update
......
\documentclass{beamer}
\usepackage{hyperref}
\usepackage{graphicx}
\usepackage{tikz}
\usetikzlibrary{positioning}
\usepackage{emoji}
\mode<presentation>
{
\usetheme{Pittsburgh}
}
\setbeameroption{show notes}
\title{VOODOO modelling progress}
\author{EcoEpi}
\begin{document}
\begin{frame}
\titlepage
\end{frame}
\begin{frame}{Outline}
\tableofcontents
\end{frame}
\section{What have we been doing?}
\begin{frame}{What have we been doing?}
\begin{itemize}
\pause
\item Effect of specialisation and resource availability on steady-state prevalence
\begin{center}
\begin{tabular}{cc}
\includegraphics[width=0.3\textwidth]{../modelling_progress_2/prevalence_handling_time_limited} &
\includegraphics[width=0.3\textwidth]{../modelling_progress_2/prevalence_search_time_limited}
\end{tabular}
\end{center}
\pause
\item Effect of transect duration on fidelity of sample-based metrics of contact network
\begin{center}
\begin{tabular}{cc}
\includegraphics[width=0.3\textwidth]{../sampling_effects/sampling_effort_narrow_diet_abundance} &
\includegraphics[width=0.3\textwidth]{../sampling_effects/sampling_effort_narrow_diet_contacts}
\end{tabular}
\end{center}
\pause
\item Trying to write this up... \emoji{sob}
\end{itemize}
\end{frame}
\section{What are we doing?}
\begin{frame}{What are we doing?}
\begin{itemize}
\pause
\item Use the flower survey data to initialise landscape sturcture
\begin{center}
\includegraphics[trim=0 250 0 250,clip,scale=0.25]{../modelling_progress_2/landscape_eunis}
\end{center}
\end{itemize}
\end{frame}
\section{Interlude: Contact networks as time-dependent graphs}
\begin{frame}{Outline}
\tableofcontents[currentsection]
\end{frame}
\begin{frame}{Time-dependent graphs}
\begin{itemize}
\item<2-> A \emph{graph} $G := (N, E)$ is a set of nodes $N$ and edges $E$
\begin{center}
\begin{tikzpicture}
\node (a) {$a$};
\node[right of=a] (b) {$b$};
\node[below of=a] (c) {$c$};
\node[below of=b] (d) {$d$};
\node[right of=d] (e) {$e$};
\draw
(a) edge[->] (b)
(a) edge[->] (c)
(a) edge[->] (d)
(b) edge[->] (e)
(d) edge[->] (b)
(d) edge[->] (c);
\only<4>
{
\draw
(a) edge[->,red,thick] (d)
(b) edge[->,red,thick] (e)
(d) edge[->,red,thick] (b);
}
\end{tikzpicture}
\end{center}
\item<3-> A \emph{time-dependent graph} $G := (N, t \mapsto E(t))$ is a set of nodes $N$ and sets of edges $E(t)$ for each time $t$
\begin{center}
\begin{tikzpicture}
\matrix[column sep=1cm, ampersand replacement=\&]
{
\node (a) {$a$};
\node[right of=a] (b) {$b$};
\node[below of=a] (c) {$c$};
\node[below of=b] (d) {$d$};
\node[right of=d] (e) {$e$};
\draw
(a) edge[->] (d)
(d) edge[->] (c);
\only<4>
{
\draw
(a) edge[->,red,thick] (d);
}
\&
\node (a) {$a$};
\node[right of=a] (b) {$b$};
\node[below of=a] (c) {$c$};
\node[below of=b] (d) {$d$};
\node[right of=d] (e) {$e$};
\draw
(a) edge[->] (c)
(d) edge[->] (b);
\only<4>
{
\draw
(d) edge[->,red,thick] (b);
}
\&
\node (a) {$a$};
\node[right of=a] (b) {$b$};
\node[below of=a] (c) {$c$};
\node[below of=b] (d) {$d$};
\node[right of=d] (e) {$e$};
\draw
(a) edge[->] (b)
(b) edge[->] (e);
\only<4>
{
\draw
(b) edge[->,red,thick] (e);
}
\\
};
\end{tikzpicture}
\end{center}
\end{itemize}
\end{frame}
\note[itemize]
{
\item point is not so much deep theorems from graph theory, but a change of perspective to extract information on potential epidemics based on the graph metrics
\item there exists a large body of literature on graphs from social sciences and engineering disciplines defining metrics and analyses
\item causal paths can be enumerated using reasonable computational effort defining the accessibility graph after time $T$, i.e. the same set of nodes $N$ and an edge from $a$ to $b$ if there exists a path starting from $a$ and ending at $b$
}
\begin{frame}{Contact networks and livestock diseases}
\begin{itemize}
\pause
\item nodes represent herds; edges represent livestock transports
\pause
\item transporting infected animals means diseases are transmitted from one herd to another
\pause
\item complex topology due to division of labour
\begin{center}
\includegraphics[width=0.8\textwidth]{pork_production}
\end{center}
\end{itemize}
\end{frame}
\note[itemize]
{
\item a lot of data on animal transports from administrative databases, e.g. \href{https://www.hi-tier.de/}{HI-Tier}
\item can be used to guide interventions, e.g. eradication, containment and prevention efforts
}
\begin{frame}{Contact networks and livestock diseases}
\begin{itemize}
\item density of paths as a predictor of the size of an outbreak; node range as a predictor of the risk of a herd
\end{itemize}
\begin{tikzpicture}
\matrix[ampersand replacement=\&]
{
\node (herd-prevalence) {\includegraphics[width=0.5\textwidth]{herd_prevalence}};
\&
\node (node-range) {\includegraphics[width=0.5\textwidth]{node_range}};
\\
};
\node[align=center,font=\tiny] at (0,-2) {Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany\\Lentz et al., \href{https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155196}{PLOS ONE 11, e0155196–32 (2016)}, \href{https://creativecommons.org/licenses/by/4.0/}{CC-BY 4.0}};
\end{tikzpicture}
\end{frame}
\begin{frame}{Contact networks and insect pathogens}
\begin{itemize}
\pause
\item nodes represent insects; edges represent co-location at flower within decontamination time
\begin{center}
\begin{tikzpicture}
\node[inner sep=1pt] (flower) {\includegraphics[width=1cm]{../dach21/yellow_flower}};
\node[inner sep=1pt,above left=of flower] (generalist) {\includegraphics[width=1cm]{../dach21/generalist}};
\node[inner sep=1pt,above right=of flower] (specialist) {\includegraphics[width=1cm]{../dach21/specialist}};
\draw
(generalist) edge[->,dashed,bend left] node[below left] {$t$} (flower)
(specialist) edge[->,dashed,bend right] node[below right] {$\leq t+T$} (flower)
(generalist) edge[->] node[above] {$t$} (specialist);
\end{tikzpicture}
\end{center}
\pause
\item visiting a flower shortly after an infected insect enables pathogen transmission
\pause
\item ephemeral topology due short time scale of decontamination
\end{itemize}
\end{frame}
\note[itemize]
{
\item takes an individual-based view on the contact network; so currently incompatible with field data
\item trait differences between insect species would be analogous to division of labour
}
\begin{frame}{Contact networks and insect pathogens}
\begin{itemize}
\item transfer methods from livestock diseases to simulation model of insect pathogens
\end{itemize}
\begin{center}
\begin{tikzpicture}
\matrix[ampersand replacement=\&]
{
\node{\includegraphics[width=0.5\textwidth]{prevalence}};
\&
\only<1>{\node{\includegraphics[width=0.5\textwidth]{path_density}};}
\only<2>{\node{\includegraphics[width=0.5\textwidth]{node_degree}};}
\only<3>{\node{\includegraphics[width=0.5\textwidth]{components}};}
\\
};
\end{tikzpicture}
\end{center}
\end{frame}
\note[itemize]
{
\item left-hand side is prevalence from simulation model as decontaminaton time, i.e. environmental stability of pathogen, is increased
\item right-hand side are path density, node degrees and component sizes of the resulting contact networks
\item two main problems so far:
\begin{itemize}
\item insects are replaced on epidemiological time scales, i.e. not just the set of edges, but also the set of nodes is time-dependent
\item small changes to graph metrics lead to large changes in e.g. steady-state prevalence
\end{itemize}
\item time-dependent graphs can also be used for post-hoc simulations which mostly mirror the a-priori ones
}
\end{document}
......@@ -189,6 +189,21 @@
abstract = {Epidemiological models for multihost pathogen systems often classify individuals taxonomically and use species-specific parameter values, but in species-rich communities that approach may require intractably many parameters. Trait-based epidemiological models offer a potential solution but have not accounted for within-species trait variation or between-species trait overlap. Here we propose and study trait-based models with host and vector communities represented as trait distributions without regard to species identity. To illustrate this approach, we develop susceptible-infectious-susceptible models for disease spread in plant-pollinator networks with continuous trait distributions. We model trait-dependent contact rates in two common scenarios: nested networks and specialized plant-pollinator interactions based on trait matching. We find that disease spread in plant-pollinator networks is impacted the most by selective pollinators, universally attractive flowers, and cospecialized plant-pollinator pairs. When extreme pollinator traits are rare, pollinators with common traits are most important for disease spread, whereas when extreme flower traits are rare, flowers with uncommon traits impact disease spread the most. Greater nestedness and specialization both typically promote disease persistence. Given recent pollinator declines caused in part by pathogens, we discuss how trait-based models could inform conservation strategies for wild and managed pollinators. Furthermore, while we have applied our model to pollinators and pathogens, its framework is general and can be transferred to any kind of species interactions in any community.}
}
@article{individual_specialization_and_multihost_epidemics,
author = {Ellner, Stephen P. and Ng, Wee Hao and Myers, Christopher R.},
title = {Individual Specialization and Multihost Epidemics: Disease Spread in Plant-Pollinator Networks},
journal = {The American Naturalist},
volume = {195},
number = {5},
pages = {E118-E131},
year = {2020},
doi = {10.1086/708272},
note ={PMID: 32364778},
URL = {https://doi.org/10.1086/708272},
eprint = {https://doi.org/10.1086/708272},
abstract = {AbstractMany parasites infect multiple species and persist through a combination of within- and between-species transmission. Multispecies transmission networks are typically constructed at the species level, linking two species if any individuals of those species interact. However, generalist species often consist of specialized individuals that prefer different subsets of available resources, so individual- and species-level contact networks can differ systematically. To explore the epidemiological impacts of host specialization, we build and study a model for pollinator pathogens on plant-pollinator networks, in which individual pollinators have dynamic preferences for different flower species. We find that modeling and analysis that ignore individual host specialization can predict die-off of a disease that is actually strongly persistent and can badly over- or underpredict steady-state disease prevalence. Effects of individual preferences remain substantial whenever mean preference duration exceeds half of the mean time from infection to recovery or death. Similar results hold in a model where hosts foraging in different habitats have different frequencies of contact with an environmental reservoir for the pathogen. Thus, even if all hosts have the same long-run average behavior, dynamic individual differences can profoundly affect disease persistence and prevalence.}
}
@article{dominant_bee_species_and_floral_abundance_drive_parasite_temporal_dynamics,
author={Graystock, Peter and Ng, Wee Hao and Parks, Kyle and Tripodi, Amber D. and Mu{\~{n}}iz, Paige A. and Fersch, Ashley A. and Myers, Christopher R. and McFrederick, Quinn S. and McArt, Scott H.},
title={Dominant bee species and floral abundance drive parasite temporal dynamics in plant-pollinator communities},
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment