Lassa Fever Agent Based Model

This is an epidemilogical stochastic agent-based model that runs locally in your browser. The model aims to simulate and visualize how infectious dieseases spread through a community. Developed by Daniel Quezada for CSUF's CEDDI Lab during URE22's summer program.

Getting Started ⚙️

To get started with this project, clone the repo with:

git clone https://github.com/DQ4781/Lassa-ABM.git

Then install the library requirements with:

pip install -r requirements.txt

To run the agent-based model, run the following command:

python3 lassa_run.py

Background 🔍

Lassa Fever (LF) is a viral hemorrhagic fever that is endemic to West Africa

• Similar to Ebola, symptoms include:
  • Fever, headaches, nausea, facial swelling, internal bleeding, seizures, and coma
• LF has been shown to have a total case fatality rate of 26.5%

Multimammate rat (mastomys natalensis) is the most common host of LF

• Primarily responsible for transmission of LF into humans
• Annually responsible for 100-300k infections every year
• Up to 30% of total rat populations are infected with LF in West Africa

Both the CDC and WHO have designated LF as a virus for priority research and are actively monitoring the situtaion in West Africa

• Gavi Institution has hinted at the possibility that LF could evolve and become the next global pandemic
• Currently, there are no vaccines or vaccine candidates for LF

Overview 📝

Data related to LF infections in Western Africa has been fitted to this model.

This model is broken up into two main parts:

1. SIR Graph

• (S)usceptible, (I)nfected, (R)ecovered graphs are a common epidemiological visualization representing how an infectious disease spreads through a population over time
• Keeps track of the percentage of humans that healthy, infected, or removed at any given time

2. Model Environment

• Keeps track of how many infected human and rodent agents there are at any given time
• Calculates the probability of infected agents transmitting the virus to a susceptible agent

Acknowledgements 🙏

Many thanks to the following individuals for assisting me throughout this project

• Ali Hussain - Research Mentor.
• Dr. Sampson Akwafuo - Primary Investigator.