Inspiration
We were inspired by the ALMAC group's problem with finding volunteers for medical studies as a result of people not wanting to travel for them.
What it does
The website takes a selection of an ailment and a needed sample size as input and outputs the optimal (shortest distance) path through major east coast cities to fulfill the sample size required.
How we built it
First, we found the populations of all the major cities using data from the US Census Bureau and the Cancer, HIV, and Alzheimer's rates for the US population, 5.4%, 0.36%, and 1.7% respectively from scientific studies. We also took into account that only 4% of the affected population is willing to participate in studies. We created a city class and objects for every city that contained their data and distances to all other cities. Then we build an adjacency graph and traversed it with DFS to find the optimal (shortest) valid (enough volunteers) path.
We used python to create various paths to different sections of the program where user input was used inside the function we built to retrieve and display values
Challenges we ran into
There were challenges with graph traversal and returning valid paths early despite them not being the optimal ones. properly keeping track of what cities were already visited was also a challenge.
Accomplishments that we're proud of
We are proud of building a fully functional website that solves real-life problems.
What we learned
We learned a lot about graph traversal and website creation and how to connect to make a functioning website.
What's next for Owl Hackathon Volunteer GPS
Hopefully our solution inspires and can be adapted for medical companies to better find volunteers.
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