Inspiration
We generally support vaccination efforts and wish to aid in those efforts by showing the potential effectiveness of vaccines. We aim to review the past couple of years by compiling the data and seeing the progress across regions. The answers received can tell us factual data, such as which areas are using which brand, and the corresponding vaccine impact in that area (or what medicine did people take while having taken the vaccine recently? (before/after)). But more reflective considerations can be drawn by looking at the data overall and seeing which sections stand out, and the overall trend of the data. The aim is to see the where, when, and why the vaccine works.
What it does
It will provide data visualization and interactive chart to help track the covid cases in US. It will also provide machine learning predict result of the new increasing covid cases
How we built it
Python3.8.6 is needed for this project, and for more dependencies, read README.md in the project folder.
Challenges we ran into
- Messy data / Multiple datasets
- New library
- Large dataset and file size
Accomplishments that we're proud of
- Making all the plot interactive, and can be export to .html and .json
- Handle with messy dataset and figure out how to merge them together
What we learned
- The Altair library
- How to reduce running time and the total data size need to be process
- Dealing with the argument between libraries and libries and in one library
What's next for Distribution and Trends of Covid cases in the US
- Collecting data contains more detail information include the brand of the vaccine. Also continue to tracking the effect on covid after booster shot is widely taken.
- Focusing on the vaccine effect on the death / severe symptoms. Create data visualization map of Covid spread in the US by time.
- Find alternative datasource of population of different states in US, this will help to get a more accurate vaccine and positive cases percentage.
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