Intro to Spatial Analytics and Epidemiological Modeling for COVID-19
Thu, April 9, 1pm – 2:30pm (PST)
Thu, April 9, 4pm – 5:30pm (EST)
Mo Chen is currently a PhD student in Environmental Engineering at University of Southern California. His research involves using data analysis and data mining techniques to understand the impact of climate change on the energy sector. Mo received a Master’s degree in Environmental Science from Peking University. He also holds an M.S. in Computer Science, specifically within the Data Science track. Mo has participated in a variety of RMDS projects, including developing DIAM evaluation models, designing Spatial Data Science courses for UC Riverside Extension program, and currently collaborating on RMDS Lab’s data science project about coronavirus.
Suyeon Ryu is currently an M.S. student in Applied Biostatistics and Epidemiology in Keck School of Medicine, University of Southern California. She is also a full-time researcher at John Wayne Cancer Institute performing molecular genome sequencing with her Next Generation Sequencing (NGS) skills. She is currently contributing to the coronavirus project with RMDS Lab to provide epidemiologic insights for the computer model development and assessments. She received recognition from the Senator of California through the award for Outstanding Community Services and continues to show her dedication to the community through her work in the field of epidemiology.
Part I: Spatial Analytics, Presented by Mo Chen
Spatial analysis plays an important role not only in our everyday life and business, but also in the fight against the ongoing coronavirus outbreak. In this webinar we will see how the concept of spatial analysis was sparked due to an epidemic event in history. We will give an overview of spatiotemporal datasets, which serve as the foundation of almost all spatial analysis including RMDS’ Project Coronavirus. Attendees will also have a chance to see how mapping acts as a powerful tool in visualizing and informing the trend of coronavirus worldwide. Lastly, some examples will be shown to illustrate how some further spatial analysis can be done, on top of spatiotemporal datasets and mapping, to give us more confidence in winning this battle.
Part II: Epidemiological Modeling, Presented by Suyeon Ryu
In this webinar, we will discuss how we have built data-driven models upon coronavirus-related data collected from multiple sources in order to track and predict the spreading trend of the virus. Specifically, we will focus on the epidemiological SIR model to simulate the development of the coronavirus in different cities. The stochastic SIR model can estimate the termination date, infection rate, recovery rate, and R0 of the coronavirus. We will discuss how we used MCMC to estimate the distribution of epidemiological parameters, and once we have the distribution of parameters the future predictions come from simulations using the Monte Carlo method.
Spatiotemporal datasets and mapping as spatial representation of coronavirus trends
Application of spatial analysis in combating epidemic event
Epidemiological terms and parameters like infection rate, recovery rate, R0, quarantine coefficient
SIR model, Bayesian statistics, Markov Chain Monte Carlo (MCMC) methods