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MEmilio is a common project between the Institute for Software Technology of the German Aerospace Center (DLR) and the department of Systems Immunology (SIMM) of the Helmholtz Center for Infection Research (HZI). This project will bring cutting-edge and compute-intensive epidemiological models to a large scale, which enables a precise and high-resolution spatiotemporal pandemic simulation for entire countries. MEmilio is still under developement but it is available as Open Source and we encourage everyone to make use of it. If you use it, please cite:
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MEmilio is a common project between the Institute for Software Technology of the German Aerospace Center (DLR) and the department of Systems Immunology (SIMM) of the Helmholtz Center for Infection Research (HZI). This project will bring cuttingedge and computeintensive epidemiological models to a large scale, which enables a precise and high-resolution spatiotemporal pandemic simulation for entire countries. MEmilio is still under developement but it is available as Open Source and we encourage everyone to make use of it. If you use it, please cite:
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M. J. Kühn, D. Abele, T. Mitra, W. Koslow, M. Abedi, K. Rack, M. Siggel, S. Khailaie, M. Klitz, S. Binder, Luca Spataro, J. Gilg, J. Kleinert, M. Häberle, L. Plötzke, C. D. Spinner, M. Stecher, X. X. Zhu, A. Basermann, M. Meyer-Hermann, "Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution". Mathematical Biosciences 339, 108648 (2021). https://www.sciencedirect.com/science/article/pii/S0025556421000845
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- Kühn, Martin Joachim und Abele, Daniel und Kerkmann, David und Korf, Sascha Alexander und Zunker, Henrik und Wendler, Anna Clara und Bicker, Julia und Nguyen, Dang Khoa und Klitz, Margrit und Koslow, Wadim und Siggel, Martin und Kleinert, Jan und Rack, Kathrin und Binder, Sebastian und Plötzke, Lena und Schmieding, René und Lenz, Patrick und Betz, Maximilian Franz und Lutz, Annette und Gerstein, Carlotta und Schmidt, Agatha und Meyer-Hermann, Michael und Basermann, Achim (2022) MEmilio - a high performance Modular EpideMIcs simuLatIOn software (2022). https://github.com/DLR-SC/memilio, https://elib.dlr.de/192140/.
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- Koslow W, Kühn MJ, Binder S, Klitz M, Abele D, et al. (2022) Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant. PLOS Computational Biology 18(5): e1010054. https://doi.org/10.1371/journal.pcbi.1010054
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**Getting started**
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This project is divided into multiple building blocks. The C++ implementation of the epidemiological models can be found in the cpp directory (see the [README](cpp/README.md) there). Currently, there is an ODE-SECIR and an agent-based model.
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MEmilio builds upon different model types, equation-based and agent-based. Furthermore, there are hybrid, graph-ODE-based models. Among the equation-based models, we provide ordinary differential equation (ODE) and integro-differential equation (IDE) based models. In order to provide highly efficient model implementations, MEmilio builds upon a C++ backend for its model and simulation-related content. Data acquisition, plotting, and machine-learnt models are provided in Python.
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Details of the C++ implementation of the epidemiological models can be found in the cpp directory (see the [README](cpp/README.md) there).
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Contact and inter-county mobility data for Germany are to be found in [data](data/README.md). Data download tools are found in the pycode folder.
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Some regularly used data for simulations of a pathogen's spread in Germany, like contact and inter-county mobility, can be found in [data](data/README.md).
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In pycode, different MEmilio python packages are defined. Via our [python bindings package](pycode/memilio-simulation), you can run our simulations from python; this package actually calls the C++ code from python. The [epidata package](pycode/memilio-epidata) provides tools to download and structure important data such as infection or mobility data. More about the python packages can be found in [Python README](pycode/README.rst).
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In pycode, different MEmilio python packages are defined. Via our [memilio-simulation](pycode/memilio-simulation) package, you can run our C++ backend from python; this package uses [pybind11](https://github.com/pybind/pybind11) to bind our C++ model code. The [memilio-epidata](pycode/memilio-epidata) package provides tools to download and structure important data such as infection or mobility data. More about the python packages can be found in [Python README](pycode/README.rst).
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**Documentation**
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Each important part of the project described above is described in detail in the README in the corresponding directory. The README contains e.g. configuration and usage instructions for users and developers.
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Also, the code is documented with doxygen and instructions on how to obtain it can be found in the docs folder.
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The documentation of the code of the master branch can be found at the following URL:
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The documentation of the code of the main branch can be found at the following URL:
The MEmilio documentation is created by [doxygen](https://www.doxygen.nl/). The documentation of the main branch is automatically deployed to https://dlr-sc.github.io/memilio/documentation/index.html by the continuous integration process.
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iUsed software: doxygen 1.8.5 (www.doxygen.nl)
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In order to create the MEmilio documentation locally, follow the procedure below.
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How to create this documentation:
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How to create the documentation:
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---------------------------------
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1. Execute
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.. code:: sh
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1. From the `docs` folder, execute
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doxygen
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This will create an html documentation
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2. Execute
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.. code:: sh
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This will create an html documentation.
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firefox html/index.html
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2. Open
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This opens the documentation.
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docs/html/index.html
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How to use the documentation
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----------------------------
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Under "Related Pages" all Readme files can be found. The title is shown there, thus it should be always meaningfull
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Under "Files" the general file structure is projected and the documentation of classes and functions etc can be found within the specific file name.
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Under "Files", you can see the general file structure and access documentation of classes.
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