Initially we were simply creating a model for our research course. It focused on how a pandemic spreads through populations at different levels of immunity. As the situation surrounding COVID-19 developed we decided to apply our model to certain aspects of the current pandemic ravaging the world. With a encouragement from our professor we decided to submit this model for this hackathon.
What it does
Our model uses agents to simulate individuals in an environment that is currently suffering a pandemic. The simulation is manipulated with the addition of barriers to emulate social distancing and isolation. The simulation can also be modified to have different levels of immunity to observe how a virus like COVID-19 will spread through the population once a vaccine is developed.
How we built it
This simulation uses COBWEB, an agent based simulation software with many built in features such as AI, Disease, etc. For our purposes we used the disease and the AI features to create an environment where agents are tightly packed to visualize the disease transmission paths. AI was modified to have the agents spin around in random directions to satisfy COBWEB's contact rules so disease transmission can occur. To show social distancing, we utilized the random stone feature of COBWEB to decrease the contact between agents. We then created different types of agents, with some that could not be infected and some that could, to show the relationship between immunization rates and viral transmission rates.
Challenges we ran into
Of course there are many problems we ran into during our project. As COBWEB is still in development, there are some features that did not work well with our model. In the most recent update the GRAVITY function was implemented; this however, crashed our model so we had to work with older versions of COBWEB. Learning COBWEB is also no simple task, primarily the AI feature of COBWEB which is highly versatile but also highly complex.
Accomplishments that we're proud of
We are proud to have created a model that matches the disease trends often found in nature.
What we learned
We learned much about disease transmission throughout our journey and learned many skills surrounding simulation.
What's next for COVID-19 Simulation in COBWEB
The next steps would be to include more of the social aspect of agents, perhaps focusing on the movement of an average person during these times. Additionally, with more information of the actual logistics such as predicted vaccine efficacy we can more accurately simulate the current pandemic.