A package for broadCASTing epidemiological and ecological models over META-populations.
MetaCast is a python package for broadcasting epidemiological and ecological ODE based models
over metapopulations (structured populations). Users first define a function describing the
subpopulation model. MetaCast's users then define the dimensions of metapopulations that this
subpopulation is broadcast over. These dimensions can be flexibly defined allowing for multiple
dimensions and migration (flows) of populations between subpopulations. In addition to the
metapopulation suite MetaCast has several features. A multinomial seeder allows users to randomly
select infected stages to place an infected population in based on the occupancy time of infected
states. MetaCast's event queue suite can handle discrete events within simulations, such as
movement of populations between compartments and changes in parameter values. Sensitivity
analysis can be done in MetaCast using parallelisable Latin Hypercube Sampling and Partial Rank Correlation Coefficient
functions. All of this makes MetaCast an ideal package not only for modelling metapopulations but
for wider scenario analysis.
Python 3.10 and pip. Package requirements:
- numpy >= 1.26.3
- pandas >= 2.1.4
- scipy >= 1.11.4
- pingouin >= 0.5.4
- tqdm >= 4.66.1
- dask >= 2024.2.1
- distributed >= 2024.2.1
For running demonstration jupyter notebooks
- bokeh >= 3.3.4
- seaborn >= 0.13.2
- jupyter >= 1.0.0
Note this should also install required packages.
pip install metacast
See jupyter notebooks in demonstration directory of the projects GitGub repository: https://github.com/m-d-grunnill/MetaCast/tree/main/demonstrations
https://metacast.readthedocs.io/en/latest/index.html
Currently, MetaCast's lhs_and_prcc function only support latin hypercube sampling with uniform distributions.
If you wish to contribute to this project please see the CONTRIBUTING.md file.