This is an epidemic that is affecting people on a global scale to the point where people don't feel comfortable leaving their homes. Its profound effects on society, economy, and global logistics has created a large scale chaos. We have already experienced similar epidemics ranging from swine flu to Ebola- there must be a way that big data can help us prepare for the future and take preventative measures.
What it does
We have narrowed down our research to 2 features we found sufficient data to work with- healthcare availability/quality and Ebola infection-rate in response to country's closing borders. The latter we wanted to test because we wanted to see if the CDC's claim that closing borders was ineffect was infact valid.
How we built it
- For our visual analysis, we used Tableau to show movement of Ebola to neighboring countries in conjunction with data of when those countries closed their borders.
- For our statistical analysis, we used a Paired T-Test for looking at if there is statistical significance between closing borders and epidemic spread.
- We used the Linear Regression Machine Learning Model to look at correlation between healthcare access and concentration of infected individuals.
We compiled our project into an easy to use UI application using Node.js.
Challenges we ran into
Finding where our skillsets could be leveraged for mutual benefit. Finding sufficient data was a huge part of our process we struggled with and dedicated time towards. Data Cleanup also took considerable time and effort.
Accomplishments that we're proud of
The breadth of technologies/software we used to accomplish a joint solution.
What we learned
TEAMWORK! TIME-MANAGEMENT! COLLABERATION! Working with data and cleaning it up to be used. Python, R, Tableau, Configuring a Custom Domain, Pandas, Sci-kit, Node-js, Git to work together
What's next for Containing The Corona Virus
Moving on to new data sets and perform experimentations to find the causation and to see which tactics work well to contain epidemic spread.