memilio | Modular spatio-temporal models
kandi X-RAY | memilio Summary
kandi X-RAY | memilio Summary
memilio is a C++ library. memilio has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
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:. 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). 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 there). Currently, there is an ODE-SECIR and an agent-based model. Contact and inter-county mobility data for Germany are to be found in data. Data download tools are found in the pycode folder. In pycode, different MEmilio python packages are defined. Via our python bindings package, you can run our simulations from python; this package actually calls the C++ code from python. The 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. 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.
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:. 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). 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 there). Currently, there is an ODE-SECIR and an agent-based model. Contact and inter-county mobility data for Germany are to be found in data. Data download tools are found in the pycode folder. In pycode, different MEmilio python packages are defined. Via our python bindings package, you can run our simulations from python; this package actually calls the C++ code from python. The 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. 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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memilio has a low active ecosystem.
It has 34 star(s) with 10 fork(s). There are 4 watchers for this library.
It had no major release in the last 6 months.
There are 179 open issues and 245 have been closed. On average issues are closed in 137 days. There are 24 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of memilio is current.
Quality
memilio has no bugs reported.
Security
memilio has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
memilio is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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memilio releases are not available. You will need to build from source code and install.
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memilio Key Features
No Key Features are available at this moment for memilio.
memilio Examples and Code Snippets
No Code Snippets are available at this moment for memilio.
Community Discussions
No Community Discussions are available at this moment for memilio.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install memilio
You can download it from GitHub.
Support
For any new features, suggestions and bugs create an issue on GitHub.
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