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