Thesis/thesis.bib

@inbook{Eichner2003,
  author    = {Eichner, Martin and Kretzschmar, Mirjam},
  pages     = {81--93},
  publisher = {Springer Berlin Heidelberg},
  title     = {Mathematische Modelle in der Infektionsepidemiologie},
  year      = {2003},
  isbn      = {9783642556128},
  booktitle = {Infektionsepidemiologie},
  doi       = {10.1007/978-3-642-55612-8_8}
}

@article{1927,
  author    = {William Ogilvy Kermack and A. G. McKendrick},
  journal   = {Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character},
  title     = {A contribution to the mathematical theory of epidemics},
  year      = {1927},
  issn      = {2053-9150},
  month     = aug,
  number    = {772},
  pages     = {700--721},
  volume    = {115},
  doi       = {10.1098/rspa.1927.0118},
  publisher = {The Royal Society}
}

@article{Foerster2024,
  author    = {Foerster, Dietrich and Bhatkar, Sayali and Bhanot, Gyan},
  title     = {Parametrization of Worldwide Covid-19 data for multiple variants: How is the SAR-Cov2 virus evolving?},
  year      = {2024},
  month     = apr,
  doi       = {10.1101/2024.04.09.24305557},
  publisher = {Cold Spring Harbor Laboratory}
}

@misc{Shaier2021,
  author    = {Shaier, Sagi and Raissi, Maziar and Seshaiyer, Padmanabhan},
  title     = {Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks},
  year      = {2021},
  copyright = {Creative Commons Attribution 4.0 International},
  doi       = {10.48550/ARXIV.2110.05445},
  keywords  = {Machine Learning (cs.LG), Quantitative Methods (q-bio.QM), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Biological sciences, FOS: Biological sciences},
  publisher = {arXiv}
}

@article{Jayatilaka2022,
  author    = {Jayatilaka, R. and Patel, R. and Brar, M. and Tang, Y. and Jisrawi, N.M. and Chishtie, F. and Drozd, J. and Valluri, S.R.},
  journal   = {Materials Today: Proceedings},
  title     = {A mathematical model of COVID-19 transmission},
  year      = {2022},
  issn      = {2214-7853},
  pages     = {101--112},
  volume    = {54},
  doi       = {10.1016/j.matpr.2021.11.480},
  publisher = {Elsevier BV}
}

@book{Zill1997,
  author    = {Dennis G. Zill},
  editor    = {Gary Ostedt},
  publisher = {Brooks/Cole Publishing Company, a division of Internaltional Thomson Publishing Inc.},
  title     = {A First Course In Differential Equations With Modelling Applications},
  year      = {1997},
  edition   = {6}
}

@book{Rudin2007,
  author    = {Walter Rudin},
  publisher = {Oldenbourg Wissenschaftsverlag GmbH},
  title     = {Analysis},
  year      = {2007}
}

@book{Tenenbaum1985,
  author    = {Morris Tenenbaum and Harry Pollard},
  publisher = {Harper and Row, Publishers, Inc.},
  title     = {Ordinary Differential Equations},
  year      = {1985}
}

@Book{EdelsteinKeshet2005,
  author    = {Leah Edelstein-Keshet},
  publisher = {Society for Industrial and Applied Mathematics},
  title     = {Mathematical Models in Biology},
  year      = {2005},
}

@Book{Anderson1991,
  author    = {Anderson, Roy Malcolm; May, Robert M.},
  publisher = {Oxford University Press},
  title     = {Infectious diseases of humans : dynamics and control},
  year      = {1991},
}

@Article{Millevoi2023,
  author    = {Millevoi, Caterina and Pasetto, Damiano and Ferronato, Massimiliano},
  title     = {A Physics-Informed Neural Network approach for compartmental epidemiological models},
  year      = {2023},
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International},
  doi       = {10.48550/ARXIV.2311.09944},
  keywords  = {Numerical Analysis (math.NA), FOS: Mathematics, FOS: Mathematics},
}

@Book{Goodfellow-et-al-2016,
  author    = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
  publisher = {MIT Press},
  title     = {Deep Learning},
  year      = {2016},
  note      = {\url{http://www.deeplearningbook.org}},
}

@Article{Lagaris1997,
  author    = {Lagaris, I. E. and Likas, A. and Fotiadis, D. I.},
  title     = {Artificial Neural Networks for Solving Ordinary and Partial Differential Equations},
  year      = {1997},
  copyright = {Assumed arXiv.org perpetual, non-exclusive license to distribute this article for submissions made before January 2004},
  doi       = {10.48550/ARXIV.PHYSICS/9705023},
  keywords  = {Computational Physics (physics.comp-ph), Cellular Automata and Lattice Gases (nlin.CG), Quantum Physics (quant-ph), FOS: Physical sciences, FOS: Physical sciences},
}

@Article{Hornik1989,
  author    = {Hornik, Kurt and Stinchcombe, Maxwell and White, Halbert},
  journal   = {Neural Networks},
  title     = {Multilayer feedforward networks are universal approximators},
  year      = {1989},
  issn      = {0893-6080},
  month     = jan,
  number    = {5},
  pages     = {359--366},
  volume    = {2},
  doi       = {10.1016/0893-6080(89)90020-8},
  publisher = {Elsevier BV},
}

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