Infectious Disease Simulator
Built during a 48-hour Hackathon Built in Visual Studio Code and Repl.it using Python, HTML, and CSS. Using linear algebra and graph theory, we built a disease spread simulator in a webpage. This collaboration was made possible by the diverse set of skills in our team.
The setup
In the wake of the COVID-19 pandemic, we were inspired to create a simulator that would serve to educate
people about
how disease spreads rapidly through a population and what measures can be taken to slow the spread. For us and
for many
other folks, the near catastrophic effects of the pandemic on our way of life was jarring to say the least. As
a group,
our team believes that, in the future, the key to an effective response is education on widespread
preventative
measures. To this effect, we created a website and accompanying simulator focused solely on educating others
on how
disease is spread and what factors play a part in its propagation.
Our simulator uses graph theory to organize and manipulate a randomized matrix in order to model a random
population of
a type determined by the user. By iterating through the matrix and using chances of infection, exposure, and
mortality,
we are able to create a model of the spread of a disease through a given population using python code and
principles of
linear algebra.
Results
Over the course of the project, we have had the privilege of learning the basics of new program languages, taking others to new unforeseen heights, and learning how to collaborate with people of varying skill levels and backgrounds. Overall, our team is proud to have made a project relevant to issues today, and that our work could have helped in any way, no matter how small, is all we could have hoped for. In the future, one of our main goals would be to rectify the errors of our first draft and create a more sophisticated model that could be used in real research and pandemic response work, in addition to education.
Reflections
Going into graph theory modeling, we knew we wanted to focus on disease modeling; likewise, we also knew that this topic was extremely prevalent today with COVID-19. The way disease spreads in the framework of our world today, is a subject deeply rooted in humanity. Different communities of socioeconomic statuses, race, and a variety of different factors respond differently to the threat of disease. As any programmer knows, mimicking and predicting humanity in lines of code is a difficult if not impossible task. This is why our project, though influenced by COVID-19, chose to take a more theoretical approach in developing a simulation. We hope that this approach is satisfactory in providing a basis for what we might create in the future.
Looking at the big picture of what we could accomplish, we want to further examine network science. A current big topic in understanding the spread of COVID-19, research into network science and the way that specific communities may transfer a disease- whether through transportation, shared living communities, or workplaces- can help contribute to a wider understanding of disease spread. Looking into the reasons why disease spread faster in certain communities and why different populations are disproportionately affected by infectious diseases is necessary both from a viewpoint of understanding infectious disease but also for learning how to better handle outbreaks and the subsequent care of the sick.
