A package for broadcasting epidemiological and ecological models over meta-populations.
Project description
MetaCast
A package for broadCASTing epidemiological and ecological models over META-populations.
Summary
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.
Installation
Requirements
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
Installing via pip
Note this should also install required packages.
pip install metacast
Usage
See jupyter notebooks in demonstration directory of the projects GitGub repository: https://github.com/m-d-grunnill/MetaCast/tree/main/demonstrations
Documentation
https://metacast.readthedocs.io/en/latest/index.html
Citation & Paper
Grunnill et al., (2024). MetaCast: A package for broadCASTing epidemiological and ecological models over META-populations.. Journal of Open Source Software, 9(99), 6851, https://doi.org/10.21105/joss.06851
Contributing
If you wish to contribute to this project please see the CONTRIBUTING.md file.
Project details
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