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

2026-02-11

AI Summary

This work proposes a unified, modular, high-performance simulation framework to address the fragmentation in current infectious disease modeling ecosystems, which hinders cross-model comparison and deployment across model types, spatial scales, and computational platforms. For the first time, the framework integrates compartmental models, meta-population models, and agent-based models within a single architecture, enabling multi-scale and comparable epidemic dynamics simulations. By standardizing representations of spatial, demographic, and mobility data, coupling a high-performance C++ core with a Python interface, and incorporating uncertainty quantification and parameter inference tools, the framework supports seamless deployment—from laptops to high-performance computing environments—significantly lowering barriers to reuse and accelerating the development of simulation-driven epidemic response capabilities.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

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.

Problem

• epidemic modeling
• software fragmentation
• model comparison
• multi-scale simulation
• infectious disease dynamics

Innovation

• modular framework
• epidemic simulation
• multi-scale modeling
• high-performance computing
• model interoperability