Overview

The MEmilio C++ project is organized as follows:

Main Directory Structure

The main directory structure in the cpp directory includes:

  • memilio/: Contains the core framework for developing epidemiological models

    • ad/: Algorithmic differentiation framework

    • compartments/: Classes for compartment models and simulations

    • data/: Data analysis functions

    • epidemiology/: Base classes for epidemiological modeling

    • geography/: Geographic region data (e.g. school vacations) and functions

    • io/: Input/output utilities for various formats (JSON, HDF5…)

    • math/: Mathematical utilities such as integrators (Euler, RK)

    • mobility/: Different Metapopulation mobility approaches

    • utils/: General helper functions (logging, etc.)

  • models/: Concrete implementation of epidemiological models

  • simulations/: Applications for scientific publications

  • examples/: Example applications demonstrating the use of the framework

  • tests/: Unit tests for framework and models

  • thirdparty/: Configuration of external dependencies

  • benchmarks/: Analyzing runtime performance

Model Structure

The MEmilio library uses a modular organization of models, where generic implementations are inherited by specific implementations:

Model Hierarchy

CompartmentalModel: The base class for all compartment-based models in MEmilio. It defines the fundamental structure for epidemiological models with compartments (e.g., SEIR, SECIR) and provides methods like eval_right_hand_side and get_initial_values required for ODE solvers.

FlowModel: Inherits from CompartmentalModel and extends it with the concept of flows between compartments. Instead of directly defining derivatives, it specifies the flows between compartments.

Specific Model Implementations:

  • ODE Model (Ordinary Differential Equations): Deterministic models for continuous populations described by ordinary differential equations.

  • IDE Model (Integro-Differential Equations): Extends the ODE model integration terms.

  • SDE Model (Stochastic Differential Equations): Adds stochastic components to model uncertainties and random effects.

Individual-based Model: Stands separate from the compartmental hierarchy and models each individual explicitly with its own properties and interactions. This enables more detailed simulations.

Build System

The project uses CMake as a build system with various configuration options. For an explanation, refer to Build instructions.