Inspiration

Two days ago, a plane carrying two sick persons landed in Philidelphia, resulting in a quarantine. Weeks ago, an entire plane was struck by an on-board illness on the way to Dubai. This year alone, there have been 9 measles outbreaks in the US, some having been linked to lack of proper vaccinations.

As the world becomes increasingly globalized and mobile, the surge of an anti-vaccination movement can present a very serious risk for widespread diseases. It is increasingly more essential for individuals to understand the risks and mechanics behind the spread of disease, which pushed us to make our project.

What it does

Epidemic is a webapp that scrapes data in real time from the internet to make several important functions. First, it is able to build a geographic heat map of the US based upon the level of anti-vaccine sentiment. Our results are consistent with many other forms of polling. As an extension to this, Epidemic also allows users to explore a simulation across time of the spread of disease along air transportation. Users can also create a custom heat map and explore different scenarios to become better aware of the risks.

How we built it

Epidemic is a django webapp, that has most of its interactive front end written in javascript. The mapping and simulation were done in javascript. Behind the front end, we wrote the data scraping, analysis, and machine learning in Python. Additionally, Epidemic runs 100% on Google's Compute cloud service.

Challenges we ran into

We ran into many challenges over API access and getting authorizations. Additionally, javascript was new for all of us, and we had very little web development experience in general. It was a large endeavor to learn as much as we could in order to create an interface that we were happy with.

Accomplishments that we're proud of

We are very happy to be able to continue our webscraping in realtime, continuously updating our map as new tweets are processed. Additionally, we are extremely proud of how close our process was able to match other research on the topic. Our risk analysis pointed out the same hotspots as several recent measles outbreaks (http://i2.cdn.turner.com/cnnnext/dam/assets/150204132250-01-vaccinations-graphics-super-169.jpg) as well as vaccination exemptions requested (https://cdn.cnn.com/cnnnext/dam/assets/150204132436-04-vaccinations-graphics-exlarge-169.jpg)

What we learned

We learned a great deal about the great uses for cloud computing. It greatly accelerated our ability to create a stable, and online project. We also learned a great deal about web development, and we are eager to continue that.

What's next for Epidemic

We hope to continually refine our data collection and disease spread simulation. It is our goal that one day, Epidemic can be more than an educational tool but a scientifically rigorous avenue for predicting the spread of disease, one that may play a large role in the mitigation of sickness.

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