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| Home > Publications Database > Software: MEmilio v1.2.0 - A high performance Modular EpideMIcs simuLatIOn software |
| Software | DZNE-2024-00912 |
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2024
Zenodo
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Please use a persistent id in citations: doi:10.5281/ZENODO.11520409
Abstract: MEmilio implements various models for infectious disease dynamics, from simple compartmental (ODE) models through Integro-Differential equation-based (IDE) models (sometimes also denoted 'age of infection models') to agent- or individual-based models (ABMs). 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. v1.2.0 Changes Added features / functionality: Stochastic differential equation based SIR and SEIR models Linear Chain Trick ODE-based model with initialization methods for real world data Automatic differentiation for ODE-based models and dynamic optimization examples Allow contact increase for simulation of larger events Allow flexible start day in IDE SECIR model Added seasonality for IDE SECIR model Alternative computation of compartments in IDE SECIR Implement initialization scheme for flows in IDE SECIR model Add Gamma distribution and other parameters to state age function for IDE models Python support for ODE SECIRVVS model Python support for 2021 metapopulation/Graph-ODE SECIRVVS simulation Age group resolution for ODE SIR and SEIR models Use ccache in CI for linux builds General changes: Use times for exposed and infected, no symptoms state in particular ODE models instead of SerialInterval and IncubationTime Updated CI actions Updated epidata readme Improve IDE SECIR model readme Handle pandas read excel engines Bundle the boost git repo instead of providing a targz archive Streamline ODE SECIR python code Corrections: Corrected handling of minimal step size in numerical integration Corrected functionality of IDE SECIR model example Prevent NaNs in newly added SDE models Resolve size_t underflow in dynamic NPIs Fix failing RKI urls Make python serialization working again Corrected IDE SECIR model simulation for certain conditions Corrected gcc compiler version in CI v1.1.0 Changes Added features / functionality: Graph simulation with metapopulation model for Munich Computation of reproduction number for ODE SECIR model Machine learnt surrogate model for ODE SECIR model with multiple age groups and contact change points Linear Chain Trick SECIR model New initialization for IDE model Unit Tests with OpenMP Corrections: Correct selection of specialized simulation and advance functions in python bindings Corrections for new MSVC Other: Expanded tests for python bindings simulations Small changes and fixes (logo, pull request template, ...) In version 1.0.0, we publish: Basic models (with local focus or without spatial resolution): four different ODE-based models from simple SIR to extended models with three subpopulations of different immunity levels and eight different compartments from asymptomatic to severe and critical disease states two IDE-based models in which more realistic transmission and compartment stays can be realized one agent-based model (ABM) which, due to its object-oriented implementation, allows for simulation of different immunity levels and multiple virus (variants)--> All models can be resolved for demographic features such as age or income. Inflow and outflow computation for compartmental modelsBasic compartmental models inherit from either a parental CompartmentalModel or a FlowModel so that new ODE-based models with standard analyses tools can be implemented time-efficient. In contrast to classical implementations of ODE-based models, FlowModels ensure a continuous computation of inflows and outflows of the compartments such that, e.g., new hospitalizations can be tracked easily. Mobility concepts which leverage basic models to spatially resolved models A deterministic mobility concept with predefined round-trip trajectories. A stochastic mobility concept which allows for non-deterministic mobility. Parameters and demographyParameters and demography are implemented by generic concepts such that they can be easily extended to more general lists of parameters or additional stratifications like age or income. Ensemble run conceptsVia standardized implementations, parameter sampling and ensemble run simulations can be conducted to assess uncertainty of the particular model outcomes. Optimizations MPI-parallel implementation of ensemble runs for parameter sampling for ODE-based models OpenMP-parallel implementation of agent-based models Optimizations towards compile-time evaluation of software parts. Helpers, utilities, math, ...MEmilio also provides a lot of mathematical algorithms, helper tools, and utilities and to simulate or analyze results. Tests and benchmarksThe MEmilio C++ backend is largely covered by software and unit tests (>95%) and benchmarks for some models are already available. A continuous integration pipeline ensures functionality of the software. Python frontend to efficient C++ backendTo open MEmilio to python developers, a variety of implemented C++ models can already be called from python via the memilio-simulation package. Python scripts for Sars-CoV-2 and demographic dataIn order to run simulations for Sars-CoV-2 in Germany, several official data sources can be downloaded and postprocessed uniformly by the memilio-epidata package. Model code generationDue to the standardized structure of compartmental models, a part of new model code can be automatically created via the memilio-generation package. Surrogate modelingWith the memilio-surrogatemodel package, expert models will be considered to be replaced by artificial intelligence and neural networks. VisualizationMEmilio also already provides certain tools for visualization of simulation results. In order to understand MEmilio, a lot of examples have already been implemented. MEmilio quality control via detailed review processes ensures validation of implemented code concepts by independent developers. For more details, see the readmes on https://github.com/SciCompMod/memilio in the particular (sub)directories.
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Software: MEmilio v1.2.1 - A high performance Modular EpideMIcs simuLatIOn software
Zenodo (2024) [10.5281/ZENODO.13341171]
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Software: MEmilio v1.1.0 - A high performance Modular EpideMIcs simuLatIOn software
Zenodo (2024) [10.5281/ZENODO.10796356]
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