Conference Program

Analyzing the Impact of Different Countries’ Approaches to the COVID-19 Pandemic on Their Cumulative Infection Curves by Using Nonparametric Density Regression and Clustering Methods (310049)

Presentation

*Damian Musk, Stanford OHS 
Mason Chen, Stanford OHS 
Dashmi Singh, Stanford OHS 
Nicholas Lu, Stanford OHS 
Edithe Lam, Stanford OHS 

Keywords: COVID-19, Phase Modeling, Nonparametric Density, Regression, Cluster Variables, Multivariate Clustering, Hierarchical Clustering

This project aims to study patterns in different national responses to the pandemic and their effect on controlling the outbreak through a nonparametric density and regression technique and various clustering tools. Seven different countries, United States of America, Italy, South Korea, Taiwan, United Kingdom, Brazil, and India were chosen for analysis due to their varied methods and successes in handling COVID-19 infection. First, a novel nonparametric density technique was utilized to accurately and consistently partition each country’s COVID-19 cumulative growth curve into different waves and phases, and each phase was modeled using linear, quadratic, or logarithmic regression. Every country’s model variables, which are the slope, r-square value, duration, and model type of each phase, were then connected to real-life factors, such as cluster outbreaks, government regulations, and the availability of healthcare resources. Multivariate correlation was conducted to uncover the relationships between model variables, and running the variable clustering algorithm showcased which model variable from a previous phase would be a good predictor for the infection situation in the following phase. Finally, based on the multivariate correlation and variable clustering results, the most important variables were used in hierarchical clustering to identify and explain the most similar and most different countries.