COVID-19 pandemic has burdened today’s healthcare, supply chain, and socio-economic systems, testing the world’s ability to make informed public health policy decisions. In the current data-driven age, can we better gauge the severity of COVID-19 infections?
The goal of our project is to study the relationship of pandemic infection spreads and human mobility. We infer COVID-19 spread characterized by publicly released Austin COVID case counts, and human mobility as harnessed from streams of data from GPS, social media, and other app services that access user locations (spatial) along with time and duration (temporal). We filter and analyze this data, extract useful information, and apply a number of cutting edge machine learning techniques to build COVID-19 forecasting models.

Team Members

Austin Chin, Jameson Kampfe, Shreyas Kudari, Renzo Teruya, Kory Yang, Xinyi (Julie) Zhu