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.
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
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
Data related to LF infections in Western Africa has been fitted to this model.
This model is broken up into two main parts:
- 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
- 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
Many thanks to the following individuals for assisting me throughout this project
- Ali Hussain - Research Mentor.
- Dr. Sampson Akwafuo - Primary Investigator.