Metadata-Version: 2.4
Name: memilio-simulation
Version: 2.3.0
Summary: Part of MEmilio project, Python bindings to the C++ libraries that contain the models and simulations.
Author: MEmilio Team
Maintainer-Email: Unknown <martin.kuehn@dlr.de>
License-Expression: Apache-2.0
Classifier: Development Status :: 5 - Production/Stable
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Project-URL: Homepage, https://github.com/SciCompMod/memilio
Project-URL: Team, https://memilio.readthedocs.io/en/latest/team.html
Requires-Python: >=3.8
Requires-Dist: numpy!=1.25.*,>=1.22
Requires-Dist: pandas>=2.0.0
Provides-Extra: dev
Description-Content-Type: text/markdown

# MEmilio - a high performance Modular EpideMIcs simuLatIOn software #

![memilio_logo](https://github.com/SciCompMod/memilio/blob/main/docs/memilio-small.png?raw=true)

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[![codecov](https://codecov.io/gh/SciCompMod/memilio/branch/main/graph/badge.svg?token=DVQXIQJHBM)](https://codecov.io/gh/SciCompMod/memilio)

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, 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

and, in particular, for

- Ordinary differential equation-based (ODE) and Graph-ODE models: Zunker H, Schmieding R, Hasenauer J, Kühn M J (2026). *Efficient numerical computation of traveler states in explicit mobility-based metapopulation models: Mathematical theory and application to epidemics*. arXiv. https://doi.org/10.48550/arXiv.2603.11275
- 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*. *Scientific Reports* 16, 6361. https://doi.org/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. https://doi.org/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. 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
