Эх сурвалжийг харах

update cluster graphics and revert bib style

FlipediFlop 9 сар өмнө
parent
commit
5e03cf57d1

+ 24 - 4
chapters/appendix/appendix.tex

@@ -11,6 +11,26 @@
 \chapter{Appendix}
 \label{chap:appendix}
 
+\begin{figure}[t]
+    \centering
+    \includegraphics[width=\textwidth]{state_sir_cluster_1.pdf}
+    \caption{Part 1 of the results}
+    \label{fig:SIR_state_results_1}
+\end{figure}
+
+\begin{figure}[t]
+    \centering
+    \begin{subfigure}{\textwidth}
+        \includegraphics[width=\textwidth]{state_sir_cluster_2.pdf}
+    \end{subfigure}
+    \quad
+    \begin{subfigure}{0.4\textwidth}
+        \includegraphics[width=\textwidth]{germany_single_sir.pdf}
+    \end{subfigure}
+    \caption{Part 1 of the results}
+    \label{fig:SIR_state_results_2}
+\end{figure}
+
 \begin{figure}[t]
     \centering
     \begin{subfigure}{0.45\textwidth}
@@ -20,7 +40,7 @@
         \includegraphics[width=\textwidth]{I_cluster_0.pdf}
     \end{subfigure}
     \caption{Part 1 of the results}
-    \label{fig:state_results_1}
+    \label{fig:I_state_results_1}
 \end{figure}
 
 
@@ -33,7 +53,7 @@
         \includegraphics[width=\textwidth]{I_cluster_1.pdf}
     \end{subfigure}
     \caption{Part 2 of the results}
-    \label{fig:state_results_2}
+    \label{fig:I_state_results_2}
 \end{figure}
 
 \begin{figure}[t]
@@ -45,7 +65,7 @@
         \includegraphics[width=\textwidth]{I_cluster_2.pdf}
     \end{subfigure}
     \caption{Part 3 of the results}
-    \label{fig:state_results_3}
+    \label{fig:I_state_results_3}
 \end{figure}
 
 \begin{figure}[t]
@@ -57,6 +77,6 @@
         \includegraphics[width=\textwidth]{I_cluster_3.pdf}
     \end{subfigure}
     \caption{Part 3 of the results}
-    \label{fig:state_results_4}
+    \label{fig:I_state_results_4}
 \end{figure}
 

+ 5 - 51
chapters/chap04/chap04.tex

@@ -53,46 +53,8 @@ representing the time span during which the COVID-19 disease was the most
 active and severe.
 
 \begin{figure}[h]
-    %\centering
-    \setlength{\unitlength}{1cm} % Set the unit length for coordinates
-    \begin{picture}(12, 9.5) % Specify the size of the picture environment (width, height)
-        \put(1.5, 4.5){
-            \begin{subfigure}{0.3\textwidth}
-                \centering
-                \includegraphics[width=\textwidth]{SIR_synth.pdf}
-                \label{fig:synthetic_SIR}
-            \end{subfigure}
-        }
-        \put(8, 4.5){
-            \begin{subfigure}{0.3\textwidth}
-                \centering
-                \includegraphics[width=\textwidth]{datasets_states/Germany_SIR_14.pdf}
-                \label{fig:germany_sir}
-            \end{subfigure}
-        }
-        \put(0, 0){
-            \begin{subfigure}{0.3\textwidth}
-                \centering
-                \includegraphics[width=\textwidth]{datasets_states/Schleswig_Holstein_SIR_14.pdf}
-                \label{fig:schleswig_holstein_sir}
-            \end{subfigure}
-        }
-        \put(4.75, 0){
-            \begin{subfigure}{0.3\textwidth}
-                \centering
-                \includegraphics[width=\textwidth]{datasets_states/Berlin_SIR_14.pdf}
-                \label{fig:berlin_sir}
-            \end{subfigure}
-        }
-        \put(9.5, 0){
-            \begin{subfigure}{0.3\textwidth}
-                \centering
-                \includegraphics[width=\textwidth]{datasets_states/Thueringen_SIR_14.pdf}
-                \label{fig:thüringen_sir}
-            \end{subfigure}
-        }
-
-    \end{picture}
+    \centering
+    \includegraphics[width=\textwidth]{in_text_SIR.pdf}
     \caption{Synthetic and real-world training data. The synthetic data is
         generated with $\alpha=\nicefrac{1}{3}$ and $\beta=\nicefrac{1}{2}$
         and~\Cref{eq:modSIR}. The Germany data is taken from the death case
@@ -370,22 +332,14 @@ is shorter, but the peak value is higher.\\
 \begin{figure}[t]
     \centering
     \begin{subfigure}{0.45\textwidth}
-        \includegraphics[width=\textwidth]{I_prediction/Thueringen_I_prediction.pdf}
-    \end{subfigure}
-    \quad
-    \begin{subfigure}{0.45\textwidth}
-        \includegraphics[width=\textwidth]{I_prediction/Bremen_I_prediction.pdf}
+        \includegraphics[width=\textwidth]{r_t_cluster_intext.pdf}
     \end{subfigure}
     \begin{subfigure}{0.45\textwidth}
-        \includegraphics[width=\textwidth]{R_t/Thueringen_R_t_statistics.pdf}
-    \end{subfigure}
-    \quad
-    \begin{subfigure}{0.45\textwidth}
-        \includegraphics[width=\textwidth]{R_t/Bremen_R_t_statistics.pdf}
+        \includegraphics[width=\textwidth]{I_cluster_intext.pdf}
     \end{subfigure}
     \label{fig:state_results}
     \caption{Visualization of the prediction of the training and  the graphs of
-        $\Rt$ for Thuringia (left) and Bremen (right) with both
+        $\Rt$ for Thuringia (upper) and Bremen (lower) with both
         $\alpha = \nicefrac{1}{14}$ and $\alpha = \nicefrac{1}{5}$. Events~\cite{COVIDChronik} like
         the peak of an influential variant or the start of the vaccination of the public are marked horizontally. Further
         visualizations can be found in~\Cref{chap:appendix}.}

+ 0 - 2
header.tex

@@ -43,8 +43,6 @@
 \usepackage{setspace}
 \onehalfspacing
 
-\usepackage[numbers]{natbib}
-
 \usepackage{cleveref}
 \usepackage{todonotes}
 \usepackage{nicefrac}

BIN
images/I_cluster_intext.pdf


BIN
images/germany_single_sir.pdf


BIN
images/in_text_SIR.pdf


BIN
images/r_t_cluster_intext.pdf


BIN
images/state_sir_cluster_1.pdf


BIN
images/state_sir_cluster_2.pdf


+ 263 - 0
thesis.bbl

@@ -0,0 +1,263 @@
+\begin{thebibliography}{10}
+
+\bibitem{WHO}
+WHO.
+\newblock Coronavirus disease (covid-19).
+\newblock \url{https://www.who.int/health-topics/coronavirus#tab=tab_1}.
+\newblock {Accessed: 2024-09-06}.
+
+\bibitem{RKI}
+RKI.
+\newblock Covid-19-strategiepapiere und nationaler pandemieplan.
+\newblock
+  \url{https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/ZS/Pandemieplan_Strategien.html}.
+\newblock {Accessed: 2024-09-06}.
+
+\bibitem{RKIa}
+RKI.
+\newblock Sars-cov-2: Virologische basisdaten sowie virusvarianten im zeitraum
+  von 2020 - 2022.
+\newblock
+  \url{https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Virologische_Basisdaten.html?nn=13490888#doc14716546bodyText10}.
+\newblock {Accessed: 2024-09-05}.
+
+\bibitem{SRD}
+{Statista Research Department}.
+\newblock Anzahl infektionen und todesfälle in zusammenhang mit dem
+  coronavirus (covid-19) in deutschland seit februar 2020.
+\newblock
+  https://de.statista.com/statistik/daten/studie/1102667/umfrage/erkrankungs-und-todesfaelle-aufgrund-des-coronavirus-in-deutschland/.
+\newblock {Accessed: 2024-09-06}.
+
+\bibitem{1927}
+William~Ogilvy Kermack and A.~G. McKendrick.
+\newblock A contribution to the mathematical theory of epidemics.
+\newblock {\em Proceedings of the Royal Society of London. Series A, Containing
+  Papers of a Mathematical and Physical Character}, 115(772):700--721, August
+  1927.
+
+\bibitem{Liu2012}
+Xinzhi Liu and Peter Stechlinski.
+\newblock Infectious disease models with time-varying parameters and general
+  nonlinear incidence rate.
+\newblock {\em Applied Mathematical Modelling}, 36(5):1974--1994, May 2012.
+
+\bibitem{Setianto2023}
+Setianto Setianto and Darmawan Hidayat.
+\newblock Modeling the time-dependent transmission rate using gaussian pulses
+  for analyzing the covid-19 outbreaks in the world.
+\newblock {\em Scientific Reports}, 13(1), March 2023.
+
+\bibitem{Shaier2021}
+Sagi Shaier, Maziar Raissi, and Padmanabhan Seshaiyer.
+\newblock Data-driven approaches for predicting spread of infectious diseases
+  through dinns: Disease informed neural networks, 2021.
+
+\bibitem{Millevoi2023}
+Caterina Millevoi, Damiano Pasetto, and Massimiliano Ferronato.
+\newblock A physics-informed neural network approach for compartmental
+  epidemiological models.
+\newblock 2023.
+
+\bibitem{Smirnova2017}
+Alexandra Smirnova, Linda deCamp, and Gerardo Chowell.
+\newblock Forecasting epidemics through nonparametric estimation of
+  time-dependent transmission rates using the seir model.
+\newblock {\em Bulletin of Mathematical Biology}, 81(11):4343--4365, May 2017.
+
+\bibitem{Berkhahn2022}
+Sarah Berkhahn and Matthias Ehrhardt.
+\newblock A physics-informed neural network to model covid-19 infection and
+  hospitalization scenarios.
+\newblock {\em Advances in Continuous and Discrete Models}, 2022(1), October
+  2022.
+
+\bibitem{Olumoyin2021}
+K.~D. Olumoyin, A.~Q.~M. Khaliq, and K.~M. Furati.
+\newblock Data-driven deep-learning algorithm for asymptomatic covid-19 model
+  with varying mitigation measures and transmission rate.
+\newblock {\em Epidemiologia}, 2(4):471--489, September 2021.
+
+\bibitem{Rudin2007}
+Walter Rudin.
+\newblock {\em Analysis}.
+\newblock Oldenbourg Wissenschaftsverlag GmbH, 2007.
+
+\bibitem{Tenenbaum1985}
+Morris Tenenbaum and Harry Pollard.
+\newblock {\em Ordinary Differential Equations}.
+\newblock Harper and Row, Publishers, Inc., 1985.
+
+\bibitem{Demtroeder2021}
+Wolfgang Demtröder.
+\newblock {\em Experimentalphysik 1}, volume~1 of {\em Lehrbuch}.
+\newblock Springer Spektrum, Berlin, 9. auflage edition, 2021.
+\newblock Auf dem Umschlag: Mit über 2,5 h Lösungsvideos zu ausgewählten
+  Aufgaben.
+
+\bibitem{Kirchhoff1845}
+Studiosus Kirchhoff.
+\newblock Ueber den durchgang eines elektrischen stromes durch eine ebene,
+  insbesondere durch eine kreisförmige.
+\newblock {\em Annalen der Physik}, 140(4):497--514, January 1845.
+
+\bibitem{Oksendal2000}
+Bernt Oksendal.
+\newblock {\em Stochastic Differential Equations}.
+\newblock Universitext Ser. Springer Berlin / Heidelberg, Berlin, Heidelberg,
+  5th ed. edition, 2000.
+\newblock Description based on publisher supplied metadata and other sources.
+
+\bibitem{EdelsteinKeshet2005}
+Leah Edelstein-Keshet.
+\newblock {\em Mathematical Models in Biology}.
+\newblock Society for Industrial and Applied Mathematics, 2005.
+
+\bibitem{Anderson1991}
+Robert~M. Anderson, Roy Malcolm;~May.
+\newblock {\em Infectious diseases of humans : dynamics and control}.
+\newblock Oxford University Press, 1991.
+
+\bibitem{Rumelhart1986}
+David~E. Rumelhart, Geoffrey~E. Hinton, and Ronald~J. Williams.
+\newblock Learning representations by back-propagating errors.
+\newblock {\em Nature}, 323(6088):533--536, October 1986.
+
+\bibitem{Goodfellow-et-al-2016}
+Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
+\newblock {\em Deep Learning}.
+\newblock MIT Press, 2016.
+\newblock \url{http://www.deeplearningbook.org}.
+
+\bibitem{Rosenblatt1958}
+F.~Rosenblatt.
+\newblock The perceptron: A probabilistic model for information storage and
+  organization in the brain.
+\newblock {\em Psychological Review}, 65(6):386--408, 1958.
+
+\bibitem{Minsky1972}
+Marvin Minsky and Seymour~A. Papert.
+\newblock {\em Perceptrons}.
+\newblock The MIT Press, Cambridge/Mass. [u.a.], 2. print. with corr edition,
+  1972.
+\newblock Literaturangaben.
+
+\bibitem{Hornik1989}
+Kurt Hornik, Maxwell Stinchcombe, and Halbert White.
+\newblock Multilayer feedforward networks are universal approximators.
+\newblock {\em Neural Networks}, 2(5):359--366, January 1989.
+
+\bibitem{Lagaris1997}
+I.~E. Lagaris, A.~Likas, and D.~I. Fotiadis.
+\newblock Artificial neural networks for solving ordinary and partial
+  differential equations.
+\newblock 1997.
+
+\bibitem{Raissi2019}
+M.~Raissi, P.~Perdikaris, and G.E. Karniadakis.
+\newblock Physics-informed neural networks: A deep learning framework for
+  solving forward and inverse problems involving nonlinear partial differential
+  equations.
+\newblock {\em Journal of Computational Physics}, 378:686--707, February 2019.
+
+\bibitem{Moseley}
+Ben Moseley.
+\newblock So, what is a physics-informed neural network?
+\newblock
+  \url{https://benmoseley.blog/my-research/so-what-is-a-physics-informed-neural-network/}.
+\newblock {Accessed: 2024-09-08}.
+
+\bibitem{GHDead}
+RKI.
+\newblock Github covid-19-todesfälle in deutschland.
+\newblock
+  \url{https://github.com/robert-koch-institut/COVID-19-Todesfaelle_in_Deutschland}.
+\newblock {Accessed: 2024-09-05}.
+
+\bibitem{GHInf}
+RKI.
+\newblock Github sars-cov-2 infektionen in deutschland.
+\newblock
+  \url{https://github.com/robert-koch-institut/SARS-CoV-2-Infektionen_in_Deutschland}.
+\newblock {Accessed: 2024-09-05}.
+
+\bibitem{Paszke2019}
+Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory
+  Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban
+  Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan
+  Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu~Fang, Junjie Bai, and Soumith
+  Chintala.
+\newblock Pytorch: An imperative style, high-performance deep learning library,
+  2019.
+
+\bibitem{FMH}
+{Federal Ministry of Health}.
+\newblock Übersicht zum impfstatus - covid-19-impfung in deutschland bis zum
+  8. april 2023.
+\newblock \url{https://impfdashboard.de/}.
+\newblock {Accessed: 2024-09-08}.
+
+\bibitem{COVInfo}
+{Federal Centre for Health Education}.
+\newblock Ansteckung, Übertragung und krankheitsverlauf.
+\newblock
+  \url{https://www.infektionsschutz.de/coronavirus/fragen-und-antworten/ansteckung-uebertragung-und-krankheitsverlauf/}.
+\newblock {Accessed: 2024-09-05}.
+
+\bibitem{COVIDChronik}
+{Federal Ministry of Health}.
+\newblock Coronavirus-pandemie: Was geschah wann?
+\newblock
+  \url{https://www.bundesgesundheitsministerium.de/coronavirus/chronik-coronavirus.html}.
+\newblock {Accessed: 2024-09-05}.
+
+\bibitem{Desson2022}
+Zachary Desson, Lukas Kauer, Thomas Otten, Jan~Willem Peters, and Francesco
+  Paolucci.
+\newblock Finding the way forward: Covid-19 vaccination progress in germany,
+  austria and switzerland.
+\newblock {\em Health Policy and Technology}, 11(2):100584, June 2022.
+
+\bibitem{Korolev2021}
+Ivan Korolev.
+\newblock Identification and estimation of the seird epidemic model for
+  covid-19.
+\newblock {\em Journal of Econometrics}, 220(1):63--85, January 2021.
+
+\bibitem{Doerre2022}
+Achim Doerre and Gabriele Doblhammer.
+\newblock The influence of gender on covid-19 infections and mortality in
+  germany: Insights from age- and gender-specific modeling of contact rates,
+  infections, and deaths in the early phase of the pandemic.
+\newblock {\em PLOS ONE}, 17(5):e0268119, May 2022.
+
+\bibitem{Cooke1996}
+K.~L. Cooke and P.~van~den Driessche.
+\newblock Analysis of an seirs epidemic model with two delays.
+\newblock {\em Journal of Mathematical Biology}, 35(2):240--260, December 1996.
+
+\bibitem{Gilbert2010}
+G.~Nigel Gilbert.
+\newblock {\em Agent-based models}.
+\newblock Number 153 in Quantitative applications in the social sciences. Sage
+  Publ., Los Angeles [u.a.], 3. pr. edition, 2010.
+
+\bibitem{Kerr2021}
+Cliff~C. Kerr, Robyn~M. Stuart, Dina Mistry, Romesh~G. Abeysuriya, Katherine
+  Rosenfeld, Gregory~R. Hart, Rafael~C. Núñez, Jamie~A. Cohen, Prashanth
+  Selvaraj, Brittany Hagedorn, Lauren George, Michał Jastrzębski, Amanda~S.
+  Izzo, Greer Fowler, Anna Palmer, Dominic Delport, Nick Scott, Sherrie~L.
+  Kelly, Caroline~S. Bennette, Bradley~G. Wagner, Stewart~T. Chang, Assaf~P.
+  Oron, Edward~A. Wenger, Jasmina Panovska-Griffiths, Michael Famulare, and
+  Daniel~J. Klein.
+\newblock Covasim: An agent-based model of covid-19 dynamics and interventions.
+\newblock {\em PLOS Computational Biology}, 17(7):e1009149, July 2021.
+
+\bibitem{Maziarz2020}
+Mariusz Maziarz and Martin Zach.
+\newblock Agent‐based modelling for sars‐cov‐2 epidemic prediction and
+  intervention assessment: A methodological appraisal.
+\newblock {\em Journal of Evaluation in Clinical Practice}, 26(5):1352--1360,
+  August 2020.
+
+\end{thebibliography}

BIN
thesis.pdf


+ 1 - 1
thesis.tex

@@ -81,7 +81,7 @@
 
 \interlinepenalty10000 % so no bib-entry will be separated by a pagebreak
 \bibliography{thesis.bib}
-\bibliographystyle{numeric} % change the bib-style if you want to
+\bibliographystyle{unsrt} % change the bib-style if you want to