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/memilioandhttps://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 AC, Plötzke L, Tritzschak H, Kühn MJ. (2024). A nonstandard numerical scheme for a novel SECIR integro differential equation-based model with nonexponentially distributed stay times. Submitted for publication.
https://arxiv.org/abs/2408.12228 - Agent-based models (ABMs): Kerkmann D, Korf S, Nguyen K, Abele D, Schengen A, et al. (2024). Agent-based modeling for realistic reproduction of human mobility and contact behavior to evaluate test and isolation strategies in epidemic infectious disease spread. arXiv.
https://arxiv.org/abs/2410.08050 - 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. (2024). Towards Graph Neural Network Surrogates Leveraging Mechanistic Expert Knowledge for Pandemic Response. arXiv.
https://arxiv.org/abs/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. arXiv.
https://arxiv.org/abs/2502.14428
Getting started
MEmilio builds upon different model types, equation-based and agent-based. Furthermore, there are hybrid, graph-ODE-based models. Among the equation-based models, we provide ordinary differential equation (ODE) and integro-differential equation (IDE) based models. In order to provide highly efficient model implementations, MEmilio builds upon a C++ backend for its model and simulation-related content. Data acquisition, plotting, and machine-learnt models are provided in Python.
Details of the C++ implementation of the epidemiological models can be found in the cpp directory (see the README there).
Some regularly used data for simulations of a pathogen's spread in Germany, like contact and inter-county mobility, can be found in data.
In pycode, different MEmilio python packages are defined. Via our memilio-simulation package, you can run our C++ backend from python; this package uses pybind11 to bind our C++ model code. The memilio-epidata package provides tools to download and structure important data such as infection or mobility data. More about the python packages can be found in Python README.
Documentation
Each important part of the project described above is described in detail in the README in the corresponding directory. The README contains e.g. configuration and usage instructions for users and developers.
Also, the code is documented with doxygen and instructions on how to obtain it can be found in the docs folder. The documentation of the code of the main branch can be found at the following URL:
https://scicompmod.github.io/memilio/documentation/index.html
Installation, Usage and Requirements
Each part has different requirements and usage. Detailed instruction can be found in the corresponding READMEs.
Development