Modeling and Comparing the COVID-19 Infection Rate for Taiwan and the United States of America Using Phase-Specific Nonparametric Density Regression Technique
Mason Chen
Stanford OHS
Patrick Giuliano
STEAMS
This project uses nonparametric density and regression to construct a technique that can accurately and consistently model different countries' cumulative growth curve through phase divisions. Previous outbreaks (SARS, MERS, and the 1918 flu pandemic) and existing models (SIR and logistic/exponential) were initially consulted to help model the growth, but the unique replication and circumstances of COVID-19 is unlike any other. Additionally, different countries have different approaches to the pandemic, and using one prediction line for the whole curve will not model the growth patterns accurately. This paper utilizes the first and second nonparametric densities to divide up the graph into separate phases and then model each phase using regression. Although each phase already provides a general picture of the different stages of the COVID-19 pandemic, Taiwan's and United States' graphs were further studied and compared to uncover other underlying patterns. The importance of factors such as strictness and timing of government regulations, testing availability, and a working contact tracing system are all reflected in the slopes and durations of each country's models.