Around the world, alcohol misuse is widely recognized as a social issue, especially among young adults. As a way to encourage the population to learn more about the impacts of their drinking behaviours, a Python implementation of a deterministic compartmental model for the pathway of alcohol in the blood was created. The compartmental pathway used in the Python code was proposed by Stephanie Lapadat and is shown below.
The Python model uses Euler's approximation technique to predict an individual's blood alcohol concentration over a set time period and displays a graph of the results as shown below.
The model allows experimentation with the following case-dependent parameters:
- Weight of subject
- Total intake of alcohol
- Number of alcohol intakes
- Average wait time between intakes
First, download BAC.ipynb. Then, open the file and before running the code, adjust the case dependent constants to your desired values.
# case dependent parameters
W = 60 # weight of individual (kg)
I_tot = 40 # total intake of alcohol (g)
n = 2 # number of intakes (including initial) between which total intake is equally divided
T = 20 # wait time between each intake, if applicable (min)
Here are just a few ways that interested individuals can contribute to this project:
- Update the values used for the universal constants in the model
- Add new case-dependent parameters for users to change
- Make model's results more accurate by accounting for even more universal constants