MONID - MODELING NETWORK FOR SEVERE INFECTIOUS DISEASES
Published March 9, 2026 | Version v2.3.0
Software Open

MEmilio v2.3.0 - A high performance Modular EpideMIcs simuLatIOn software

Authors/Creators

  • 1. ROR icon Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
  • 2. ROR icon University of Bonn
  • 3. Helmholtz Centre for Infection Research (HZI)
  • 4. ROR icon Forschungszentrum Jülich
  • 5. EDMO icon German Aerospace Center

Contributors

  • 1. ROR icon Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
  • 2. ROR icon University of Bonn
  • 3. Helmholtz Centre for Infection Research (HZI)
  • 4. ROR icon Forschungszentrum Jülich

Description

MEmilio implements various models for infectious disease dynamics, ranging from simple compartmental models to complex Integro-Differential and agent-based models. Its modular design enables the combination of different models with distinct mobility patterns. Through efficient implementation and parallelization in C++ and an easy-to-use python interface, MEmilio delivers cutting-edge and compute-intensive epidemiological models to broad range of applications and users, providing precise and high-resolution spatiotemporal infectious disease dynamics. MEmilio is continuously extended and is available open-source for community use.

We here publish version 2.3.0, our most recent major release.

v2.3.0 Changes

Added features / functionality:

  • Added SEIRDB model for EBOLA
  • Added Influenza model
  • Added LCT SECIR model with two disease strains
  • Enable SMM Model with generic indices
  • Generalize ParameterStudy for ODE models in python bindings
  • Simplify parameter IO and graph building with constructor
  • Make timers MPI compatible
  • Added Python bindings for get_times() function
  • Improved compile time of flows

General changes:

  • Deactivate SBML support by default
  • Add CMake option to allow building python simulation without HDF5
  • Accelerate add_edge() for large graphs
  • Normalize transitions in ode MSEIRS4 model
  • Migrate Python packages to use pyproject.toml for builds
  • Enable numpy versions 1.22-1.24
  • Update Python Version in CI to 3.12
  • Read result from HDF5 files uses lexical order

Corrections:

  • Correction for SMM model output misaligned with time points
  • Detection of duplicate flows in the FlowModel
  • Fix documentation build after migration to pyproject toml
  • Use correct ParameterStudy in graph examples of Python bindings
  • Change default value of MaxRiskOfInfectionFromSymptomatic
  • Correct non-ageresolved indices in IDE-SECIR model
  • Correction of interpolate_simulation_result which did not yield consistent timepoints
  • Correction of memilio.simulation incompatible with pandas 3

Minor changes:

  • Improve messages related to parameter checks
  • Update citation file
  • General documentation improvement
  • Modernizations and minor fixes in core C++ library
  • Minor additions to how infections in the ABM work
  • Revise README for data directory clarification
  • Updated holiday data

 

For more details, see https://github.com/SciCompMod/memilio and our novel documentation: https://memilio.readthedocs.io/en/latest/ 

Files

memilio-2.3.0.zip

Files (2.8 MB)

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Additional details

Identifiers

arXiv
arXiv:2602.11381v1

Is described by
Publication: 10.48550/arXiv.2602.11381 (DOI)
Is part of
Publication: 10.1371/journal.pcbi.1012630 (DOI)
Publication: 10.1016/j.amc.2025.129636 (DOI)
Publication: 10.1016/j.compbiomed.2025.110269 (DOI)
Publication: 10.1016/j.idm.2024.12.015 (DOI)
Publication: 10.48550/arXiv.2411.06500 (DOI)
Publication: 10.48550/arXiv.2412.09140 (DOI)
Publication: 10.1016/j.chaos.2025.116782 (DOI)
Publication: 10.1016/j.chaos.2026.118179 (DOI)
Publication: 10.1038/s41598-026-39431-5 (DOI)

Funding

Federal Ministry of Transport and Digital Infrastructure
PANDEMOS: Pandemic Mobility Scenarios FKZ19F2211A
Helmholtz Association of German Research Centres
LOKI: Integrated Early Warning System for Local Recognition, Prevention, and Control for Epidemic Outbreak KA1-Co-08
Federal Ministry of Education and Research
INSIDe: Integrative modelling of the spread of serious infectious diseases 031L0297B
Helmholtz Association of German Research Centres
Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) 0
Deutsche Forschungsgemeinschaft
MESID 528702961
Federal Ministry of Education and Research
AIMS 031L0319A & 031L0319B
Federal Ministry of Education and Research
EPISERVE 031L0324E
Federal Ministry of Education and Research
TwinChain 031L0325A

Dates

Available
2023-12-20
Initial release
Available
2024-03-08
v1.1.0
Available
2024-06-07
v1.2.0
Available
2024-08-19
v.1.2.1
Available
2024-11-28
v1.3.0
Available
2025-04-07
v2.0.0
Available
2025-08-01
v2.1.0
Available
2025-11-17
v2.2.0
Available
2026-03-09
v2.3.0

Software

Repository URL
https://github.com/SciCompMod/memilio
Programming language
C++ , Python
Development Status
Active

References