suteli / covid19_challenge

This is a data analysis project of COVID-19 for UC Davis MSBA Hackathon. This project aims to explore the effectiveness of wearing masks/awareness of wearing masks against COVID-19.

COVID-19 Data Analysis

• Team Disinfectant Wipes: Jiangfan Jing, Jing Li, Stella (Sute) Li, Boya Sun

This is a panel analysis project on COVID-19 related data for UC Davis MSBA Hackathon. We won the 5th place among the finalists.

Our project aims to quantify the effect of the awareness of wearing a mask in slowing the spread of COVID-19. Leveraging Google Trends and data on stay-at-home orders and medical resources, we built a fixed effect panel regression model and found that the increase in the awareness of wearing a mask (quantified by search interest on Google) was the most associated with lowering the increase in confirmed cases of COVID-19. That is to say, when people start to search for masks, that' s when they truly became aware of how serious the pandemic situation is, and started to take precautions. Hence, we recommended that state governments should advise (if not require) all citizens to wear masks in public as early as they could.

A presentation deck summarizing our findings can be found here .