Agent-Based Modeling: Meningitis

This is a Python implementation using the Starsim package. We implement a SIR-like model. Is extended to SEIRs:

- Susceptible
- Exposed 
- Infected
- Recovered
- etc  

Creating an intuitive GUI and the backend the server in Django, to set model parameters with minimum effort and view impactive visualizations for data-driven decisions, not gut feelings :)

Premise

Mathematical and computational models have become increasingly central in epidemiological research, and particularly for infectious diseases. This solution is a system that bridges the gap between mathematical modelling and real-world problem domains to provide insights into how infection works in a population and gauge how intervention strategies (e.g. vaccination, treatment) can be used in the mix to counter infection. This would be used to make data-driven decisions or influence healthcare or lifestyle choices for individuals, agencies, organizations and even governments. In this context we use Agent-Based Modeling.

WHAT Agent-Based Modeling IS

Extending SIR to SEIRs

Think of Agent-Based Modeling as a system of computational models that simulate behavior of individual agents in order to study emergent phenomena - in terms of disease infection in a population. Agents may represent humans, institutions, microorganisms, and disease vectors and so forth. The agents’ actions are based on autonomous decision-making and other behavioral traits, implemented through formal rules of interaction in an environment.
Agent-based modeling provides a unique lens through which complex systems can be examined and understood. This allows for manipulating numerous variables to create detailed scenarios, offering insights into how different vaccination strategies, and treatment might perform under various epidemiological and social conditions.

Read More on Agent-Based Modeling: epidemix.jhubafrica.com

Starsim

Starsim is an agent-based modeling framework in which users can design and configure simulations of diseases (or other health states) that progress over time within each agent and pass from one agent to the next along dynamic transmission networks. The framework explicitly supports co-transmission of multiple pathogens, allowing users to concurrently simulate several diseases while capturing behavioral and biological interactions. We use Starsim for the modeling and the simulating part of this project.

---

Environment Set-Up

It's recommended to leverage a virtual environment whenever using Python for projects. This keeps your dependencies for each project separate and organized. Instructions for setting up a virtual environment for your platform can be found in the Python Docs.

• Create a siloed virtual environment with Python 3.12 and activate it. You should have Python 3.12 available in your host/local machine. Check the Python path using:

$ which python3

$ python3 -m pip install --user virtualenv 
# on parent of root Django project directory;
# use a command similar to this one to create environment:
$ python3 -m virtualenv --python=<path-to-python3.12> ../.dj_sim
$ source ../.dj_sim/bin/activate

Alternatively, you could setup the virtual environment via make setup in this Makefile.

• Run make install to install Django, Starsim and other dependencies for it. This will install all relevant pip packages for the project.

$ cd meningitis_sim
$ code .
$

---

Running Simulations

Each simulation runs in a Django environment:

# python manage.py makemigrations
# python manage.py migrate
# python manage.py runserver

(.dj_sim) mu-o@HP:~/django_sim/meningitis_sim$ python manage.py runserver
Watching for file changes with StatReloader
Performing system checks...

Starsim 0.3.4 (2024-04-18) — © 2023-2024 by IDM
System check identified no issues (0 silenced).
June 12, 2024 - 11:09:26
Django version 5.0.6, using settings 'meningitis_sim.settings'
Starting development server at http://127.0.0.1:8000/
Quit the server with CONTROL-C.

---

Run Simulations - Docker

One caveat to this Starsim setup is: this simulation environments won't run properly on windows - I cannot give a full explanation on this but it probably has to do with handling processes/threading.

You can use containers: view the Dockerfile for the container setup.

docker build -t meningitis_sim .

# run django/simulation in container
docker run -d -p 8000:8000 --name sim meningitis_sim

View the app in-browser: http://localhost:8000/