MEmilio -- A high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics

TL;DR

MEmilio is a modular, high-performance framework for epidemic simulation that harmonizes the specification and execution of diverse dynamic epidemiological models within a unified and harmonized architecture, and aims to lower barriers to reuse and generalize models, enable principled comparisons of implicit assumptions, and accelerate the development of novel approaches that strengthen modeling-based outbreak preparedness.

Abstract

Epidemic and pandemic preparedness with rapid outbreak response rely on timely, trustworthy evidence. Mathematical models are crucial for supporting timely and reliable evidence generation for public health decision-making with models spanning approaches from compartmental and metapopulation models to detailed agent-based simulations. Yet, the accompanying software ecosystem remains fragmented across model types, spatial resolutions, and computational targets, making models harder to compare, extend, and deploy at scale. Here we present MEmilio, a modular, high-performance framework for epidemic simulation that harmonizes the specification and execution of diverse dynamic epidemiological models within a unified and harmonized architecture. MEmilio couples an efficient C++ simulation core with coherent model descriptions and a user-friendly Python interface, enabling workflows that run on laptops as well as high-performance computing systems. Standardized representations of space, demography, and mobility support straightforward adaptations in resolution and population size, facilitating systematic inter-model comparisons and ensemble studies. The framework integrates readily with established tools for uncertainty quantification and parameter inference, supporting a broad range of applications from scenario exploration to calibration. Finally, strict software-engineering practices, including extensive unit and continuous integration testing, promote robustness and minimize the risk of errors as the framework evolves. By unifying implementations across modeling paradigms, MEmilio aims to lower barriers to reuse and generalize models, enable principled comparisons of implicit assumptions, and accelerate the development of novel approaches that strengthen modeling-based outbreak preparedness.

Figures (6)

• Figure 1: Overview of open-source, state-of-the-art infectious disease modeling frameworks, highlighting the distinctions of MEmilio.a) Overview of 21 software frameworks, depicting which model types are available through the software, which programming language has been used, if parallelization techniques are provided, which license is given, and, if unit and software tests are implemented, which code coverage is achieved. b) Statistical overview of the 10 most used programming languages in public GitHub repositories according to the search terms: SARS-CoV-2, COVID-19, influenza, Ebola, HIV, malaria, transmission, epidemiological, infection, pandemic, epidemic, endemic, pandemic spread, epidemic spread, endemic spread (accessed through the GitHub API). c) Overview of active development of MEmilio, expanding for new applications and features.
• Figure 2: Overview of the MEmilio software framework. The MEmilio software framework consists of an efficient C++ backend (center) which provides models of different types, granularity and application foci. Several optional modules (left and right) provide additional functionality to be used from MEmilio's C++ or Python interface. In the bottom row, we depict the execution on consumer laptops or supercomputing infrastructure, together with the typical external packages that can be integrated into a modeling workflow.
• Figure 3: Detailed overview of PBMs, MPMs, and ABMs in MEmilio. Models are shown from finest (top) to coarsest (bottom) granularity. The first column shows the individual (ABM), regionally heterogeneous (MPM) or aggregated (PBM) disease courses, with the corresponding spatial resolution in the second column. The necessary data items with required level of detail are shown in the third and fourth column. Exemplary research questions are given in the rightmost column.
• Figure 4: Models and calibrations for different diseases and spatio-temporal dynamics together with an evaluation of intervention strategies.a) SIRS, SEIRDB, and SECIR-type course of disease structure for modeling influenza, Ebola, and early SARS-CoV-2, respectively. b) Fit of the SDE SIRS-type influenza model for Germany. c) Fit of the ODE SEIRDB-type Ebola model for the outbreak in Guinea in 2014. d) Fits of the ODE SECIR-type model for SARS-CoV-2 related ICU cases in Germany for national (top) and district (bottom) level. e) Fits of the SECIR-type model for SARS-CoV-2 related ICU cases in Spain for national (top) and provinces (bottom) level. f) Evolution of infected cases for five different global and local intervention strategies for Germany. g) Evolution of continued NPIs and DynamicNPIs for three randomly selected districts (in color for Flensburg, in gray for Aachen and Heilbronn). h) Evolution of infected, severe, and critical cases for DynamicNPIs with optimal control. i) Summary of the five intervention strategies with respect to the average number of realized contacts and outcomes for infected, severe, and critical cases. Shape files for Germany using geodata 'Verwaltungsgebiete 1:250 000 (VG250)" from BKG (2026) dl-de/by-2-0, data sources: https://sgx.geodatenzentrum.de/web_public/gdz/datenquellen/datenquellen_vg_nuts.pdf. Shape files for Spanish administrative units CC-BY 4.0 ign.es.
• Figure 5: PBM and ABM in comparison with temporal-hybrid ABM-PBM model and inter-model comparison with different state transition assumptions and contact structures. a) Schematic visualization of the temporal-hybrid model for a SEIR model with colors as in \ref{['fig:different_models_and_questions']}. b) Median and CI through 5th and 95th percentiles for 1 000 runs of all three models for the whole simulated time frame (top) and median and 90 % CI of the simulations with virus extinction (bottom). c) Initialization and simulation runtime for all three models, with the pie chart showing the percentage of the temporal-hybrid model's simulation runtime relative to the ABM's simulation runtime. d) Settings used for the inter-model comparison of ABM, ODE, LCT, IDE. e) Trajectories of the simulated number of Exposed, ICU, and Dead for ABM, ODE, LCT, IDE for exponential (S1) and lognormal (S2) transition distributions using one (S1.1, S2.1) or multiple (S1.2, S2.2) locations per type in the ABM. For the ABM, median and 90 % CI across 100 runs are shown.