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@@ -9,7 +9,7 @@ MEmilio implements various models for infectious disease dynamics, from simple c
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If you use MEmilio, please cite our work
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- Bicker J, Kerkmann D, Korf S, Plötzke L, Schmieding R, Wendler A, Zunker H et al. (2025) *MEmilio - a High Performance Modular Epidemics Simulation Software*. Available at https://github.com/SciCompMod/memilio and https://elib.dlr.de/219141/ .
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- Bicker J, Gerstein C, Kerkmann D, Korf S, Schmieding R, Wendler A, Zunker H et al. (2026) *MEmilio - A high performance Modular Epidemics Simulation software for multi-scale and comparative simulations of infectious disease dynamics*. Submitted for publication. https://doi.org/10.48550/arXiv.2602.11381
.. |MEmilio_citation| replace:: Bicker J, Gerstein C, Kerkmann D, Korf S, Schmieding R, Wendler A, Zunker H, Abele D, Betz M, Nguyen K, Plötzke L, Volmer K, SchmidtA, Waßmuth N, Lenz P, Richter D, Tritzschak H, Hannemann-Tamas R, Litz J, Johannssen P, Borges M, Jungklaus A, Heger M, Lange A, Kluth E, Rack K, Wieland V, Arruda J, Binder S, Klitz M, Siggel M, Dahmen M, Basermann A, Meyer-Hermann M, Hasenauer J, Kühn MJ. (2026). *MEmilio -- A high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics*. Submitted for publication. |MEmilio_DOI|_
.. |Graph_Neural_Network_Surrogates| replace:: Schmidt A, Zunker H, Heinlein A, Kühn MJ. (2025). *Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response*. Submitted for publication. |Graph_Neural_Network_Surrogates_DOI|_
.. |Graph_Neural_Network_Surrogates| replace:: Schmidt A, Zunker H, Heinlein A, Kühn MJ. (2026). *Graph neural network surrogates to leverage mechanistic expert knowledge towards reliable and immediate pandemic response*. *Scientific Reports*. |Graph_Neural_Network_Surrogates_DOI|_
.. |Revisiting_the_Linear_Chain| replace:: Plötzke L, Wendler A, Schmieding R, Kühn MJ. (2024). *Revisiting the Linear Chain Trick in epidemiological models: Implications of underlying assumptions for numerical solutions*. Mathematics and Computers in Simulation 239, pp. 823-844 (2026). |Revisiting_the_Linear_Chain_DOI|_
Maximilian Betz completed his Bachelor's degree in Computer Science at the DHBW Mannheim as an integrated degree program with the Department of High-Performance Computing at the German Aerospace Center (DLR). Afterwards, he continued with a Master's in Computer Science at the University of Cologne, where he focused on Machine Learning and High Performance Computing. During the Master's, he kept working at the DLR as a student assistant with a focus on automatic generation of Python bindings, metapopulation models to simulate the spatial spread of infectious diseases and Machine Learning based parameter inference.
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* |MEmilio_citation|
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René Schmieding
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^^^^^^^^^^^^^^^
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* |MEmilio_citation|
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* |Novel_travel_time_aware_metapopulation_models|
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* |Hybrid_metapopulation_agent-based|
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* |MEmilio_citation|
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* |Agent-based_modeling_for|
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* |MEmilio_citation|
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* |Assessment_of_effective_mitigation|
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.. dropdown:: Links
@@ -344,6 +367,7 @@ Agatha Schmidt completed her Master's degree at the University of Cologne in col
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