Inspiration - My teammates and I are all passionate about social justice and reform. We wanted to create a program that can benefit each and every individual within our nation, regardless of ethnical, economical, and other underlying factors that may deprive individuals of their ability to obtain proper PPE. We hope that this program can benefit both patients and medics alike, as we unite as a nation to battle this ongoing pandemic.

What it does - It analyzes patient information to generate a critical scale for each patient, which then calculates the required number of equipment and their corresponding type for that patient. This information will be collective across all hospitals within a state to map the general needs for the state, and across all states within the nation to pass it onto a federal level.

How we built it - we built it through java, javascript, and html

Challenges we ran into - Initial attempts to implement machine learning: we were unable to access sufficient and adequate training data for the neural network program. Thus, we had to improvise and implement a different algorithm to calculate a patient's critical level. Implementing the visuals were a lot more difficult than writing the code that represents the raw source files.

Accomplishments that we're proud of - The scale of this project is beyond anything we have ever accomplished in the past: from brainstorming ideas to developing its solutions to writing the codes to implementing the visuals. We are proud of the collaborative effort from our researchers to computer scientists that made this happen. We are also glad that we can come up with ideas that help the nation as a whole during these difficult times.

What we learned - The difficulties and challenges of developing a full front to back end software application as well as the pride after developing a working program.

What's next for COVID Resource Manager - Instead of using math to hardcode the general rules for what classifies a patient as critical, we want to be able to use machine learning in order to more accurately and flexibility calculate the critical stage of Patients.

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