Members
Lifan Liao, Tsz Wai Tsui, Ruolin Chen
Inspiration
By analyzing the data sets regarding death by COVID-19 and vaccination, we can potentially see the effectiveness of vaccination and also the severity of the new variance of the virus. The result of the analysis can be used to predict the trend of COVID-19 influence in the U.S. or a particular state in the U.S. To some extent, the result can also help to guide the distribution of resources. For example, if one state has a particularly high death rate regarding COVID-19, people may spend more focus and distribute more medical resources to that state in order to reduce the death rate. In addition, the analysis regarding the age group may determine discontinuity lines of death rate that separates different age groups. People may use the result to estimate the potential risk of exposure for a certain age group. The analysis regarding sexes can work the same way to determine if a sex group takes more risk of death after infected by COVID-19, or there is no differences. Moreover, a richer analysis that includes extra factors, such as population density and GDP per capita, is able to provide a more detailed view of the distribution of deaths due to COVID-19. We can use the result to, to some extent, predict the death rate of a certain state by giving some parameters, so that people may apply it to try to approach a lower death rate due to COVID-19 by actively controlling the influential factors.
What it does
It analyzes several relationships regarding covid death rate in the US, containing several visualizations and ML model.
How we built it
Python teamwork
Challenges we ran into
multiple datasets, ML, messy data
Accomplishments that we're proud of
draw relationships and insights
What's next for US Covid Death & Vaccine Analysis
more parameters, higher resolution, update over time
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