Citing MEmilio

If you use MEmilio, please cite our work

  • 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. DOI:10.48550/arXiv.2602.11381

and, in particular, for

  • Ordinary differential equation-based (ODE) and Graph-ODE models: Zunker H, Schmieding R, Kerkmann D, Schengen A, Diexer S, Mikolajczyk R, Meyer-Hermann M, Kühn MJ. (2024). Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios. PLOS Computational Biology 20(12): e1012630. DOI:10.1371/journal.pcbi.1012630

  • Integro-differential equation-based (IDE) models: Wendler A, Plötzke L, Tritzschak H, Kühn MJ. (2026). A nonstandard numerical scheme for a novel SECIR integro differential equation-based model with nonexponentially distributed stay times. Applied Mathematics and Computation 509: 129636. DOI:10.1016/j.amc.2025.129636

  • Agent-based models (ABMs): Kerkmann D, Korf S, Nguyen K, Abele D, Schengen A, Gerstein C, Göbbert JH, Basermann A, Kühn MJ, Meyer-Hermann M. (2025). Agent-based modeling for realistic reproduction of human mobility and contact behavior to evaluate test and isolation strategies in epidemic infectious disease spread. Computers in Biology and Medicine 193: 110269. DOI:10.1016/j.compbiomed.2025.110269

  • Hybrid agent-metapopulation-based models: Bicker J, Schmieding R, Meyer-Hermann M, Kühn MJ. (2025). Hybrid metapopulation agent-based epidemiological models for efficient insight on the individual scale: A contribution to green computing. Infectious Disease Modelling 10(2): 571-590. DOI:10.1016/j.idm.2024.12.015

  • Graph Neural Networks: 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 16, 6361. DOI:10.1038/s41598-026-39431-5

  • ODE-based models with Linear Chain Trick: Plötzke L, Wendler A, Schmieding R, Kühn MJ. (2026). Revisiting the Linear Chain Trick in epidemiological models: Implications of underlying assumptions for numerical solutions. Mathematics and Computers in Simulation 239, pp. 823-844. DOI:10.1016/j.matcom.2025.07.045

  • Behavior-based ODE models: Zunker H, Dönges P, Lenz P, Contreras S, Kühn MJ. (2025). Risk-mediated dynamic regulation of effective contacts de-synchronizes outbreaks in metapopulation epidemic models. Chaos, Solitons & Fractals 199:116782. DOI:10.1016/j.chaos.2025.116782