MEmilio - a high performance Modular EpideMIcs simuLatIOn software

MEmilio implements various models for infectious disease dynamics, from simple compartmental models through Integro-Differential equation-based models to agent- or individual-based models. Its modular design allows the combination of different models with different mobility patterns. Through efficient implementation and parallelization, MEmilio brings cutting edge and compute intensive epidemiological models to a large scale, enabling a precise and high-resolution spatiotemporal infectious disease dynamics. MEmilio will be extended continuously. It is available open-source and we encourage everyone to make use of it.

If you use MEmilio, please cite our work

• 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/213614/ .

and, in particular, for

• Ordinary differential equation-based (ODE) and Graph-ODE models: Zunker H, Schmieding R, Kerkmann D, Schengen A, Diexer S, et al. (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. https://doi.org/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. https://doi.org/10.1016/j.amc.2025.129636
• Agent-based models (ABMs): Kerkmann D, Korf S, Nguyen K, Abele D, Schengen A, et al. (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. https://doi.org/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. https://doi.org/10.1016/j.idm.2024.12.015
• Graph Neural Networks: 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. https://doi.org/10.48550/arXiv.2411.06500
• ODE-based models with Linear Chain Trick: Plötzke L, Wendler A, Schmieding R, Kühn MJ. (2024). Revisiting the Linear Chain Trick in epidemiological models: Implications of underlying assumptions for numerical solutions. Submitted for publication. https://doi.org/10.48550/arXiv.2412.09140
• 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. https://doi.org/10.1016/j.chaos.2025.116782

Getting started

The documentation for MEmilio can be found at https://memilio.readthedocs.io/en/latest/index.html

Publication simulations

Simulations used for publications, along with their specific plotting and processing scripts, are available in a separate repository: https://github.com/SciCompMod/memilio-simulations

Development

The coding guidelines and git workflow description can be found in the documentation at https://memilio.readthedocs.io/en/latest/development.html