Prep4Pandemic

Our project will be able to tell the user the most prevalent diseases from whichever states are chosen.

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Inspiration

In light of the recent pandemic and the way that many governments weren't able to handle the allocation of resources properly we thought it would be a good idea to create an application that would help the government visualize the allocation of these resources.

What it does

The application outputs a list of the most prevalent diseases from each appropriate state that is selected by the user.

How we built it

We built the backend utilizing python and pandas. The UI was built utilizing flask and bootstrap.

Challenges we ran into

We ran into challenges picking a dataset that we found fitting when it came to displaying the right results that we wanted.

Accomplishments that we're proud of

We're proud to have created an application that successfully achieves this and allows the user to be able to retrieve a list of the most prevalent diseases in each state. We were new to bootstrap and flask, and we were able to learn them and create an application.

What we learned

Through this, we realized that public health datasets are able to portray a lot of helpful and powerful information that can be used for a lot of good. In our case helping the government allocate the proper resources to the appropriate states for use.

What's next for Prep4Pandemic

We could add more features in terms of statistics like how many cases are in the state for the outputted diseases. We could also have a feature that may tell the user what diseases have been increasing in case count by a significant amount in a short amount of time.

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Updates

posted an update —

Why aggregate claims data? Because it reflects the prevalence of the conditions well, because the people needed those services. This can help inform things like how vulnerable the population overall is, what are some of the higher risk areas, and what are the more urgent areas to make programs in. Taking this further, this data can also serve as a baseline check for whether the stocked up supplies that the organization has would be enough. But that would require people more knowledgeable on what are the specific needs (supplies) for people with specific conditions. For example, if there is high prevalence of Respiratory Diseases, the organization might want to stock up on masks and create air quality programs. The current work is per state data, but the data could refined if more details on location is available.

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