https://github.com/dmnfarrell/btbabm
Agent based model for bovine TB spread in herds
https://github.com/dmnfarrell/btbabm
agent-based-modeling bovine-tb epidemiology
Last synced: 2 months ago
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Agent based model for bovine TB spread in herds
- Host: GitHub
- URL: https://github.com/dmnfarrell/btbabm
- Owner: dmnfarrell
- License: apache-2.0
- Created: 2023-02-20T20:33:45.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-21T10:35:43.000Z (over 2 years ago)
- Last Synced: 2025-03-12T03:28:16.129Z (over 1 year ago)
- Topics: agent-based-modeling, bovine-tb, epidemiology
- Language: Jupyter Notebook
- Homepage:
- Size: 12.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# bTBabm - Agent based model for bovine TB spread in herds.
This is a Python package that simulates Bovine TB spread amongst herds and badgers. It uses the Mesa package for implementing an Agent based model. This code is experimental and for research purposes only. Code may be subject to significant change.
## Parameters
Times are given in days since each step is a day.
* mean_stay_time - mean time in a herd
* mean_inf_time - mean infection time
* mean_latency_time - mean latency time when not infectious
* cctrans - cow-cow transmission prob
* bctrans - badger-cow transmission prob
* infected_start - how many cows to infect at start
* mean_inf_time - mean infection time length before death
* mean_stay_time - mean time on farm
* seq_length - sequence length for simulating strains/mutations
* herd_class - type of herd
## Installation
If you want to try this out, the easiest way is to install with pip:
`pip install -e git+https://github.com/dmnfarrell/btbabm.git#egg=btbabm`
## Usage
In Python you can run the model as follows:
```python
from btbabm import models
from btbabm import utils
model = models.FarmPathogenModel(F=30,C=800,S=10,mean_inf_time=20,mean_stay_time=150,
cctrans=0.01,seq_length=100,graph_seed=4)
#run 100 steps
for s in range(100):
model.step()
#equivalent code with progress bar
model.run(100)
#get state data
df = model.get_column_data()
#get data for infected animals
df = model.get_infected_data()
#plot the grid/network
fig,ax=plt.subplots(1,1,figsize=(10,6))
utils.plot_grid(model,with_labels=True,ns='perc_infected',ax=ax)
```
## Dashboard
There is a panel dashboard for experimenting with the model. It can be run by executing the `dashboard.py` module.
## References
* [Mesa](https://mesa.readthedocs.io/)
* [Individual-based model for the control of Bovine Viral Diarrhea spread in livestock trade networks](https://www.sciencedirect.com/science/article/pii/S0022519321002393?via%3Dihub)
* [A Practical Introduction to Mechanistic Modeling of Disease Transmission in Veterinary Science](https://www.frontiersin.org/articles/10.3389/fvets.2020.546651/full)