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Activity Number: 200 - Statistical Methods in Policy Evaluation: From COVID-19 to Medical Cannabis–Related Policy
Type: Contributed
Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #320870
Title: 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
Author(s): Edithe Lam* and Dashmi Singh and Nicholas Lu and Mason Chen and Damian Musk
Companies: Stanford OHS and Stanford OHS and Stanford OHS and Stanford OHS and Stanford OHS
Keywords: COVID-19; Phase Modeling; Nonparametric Density; Regression; Cluster Variables; Hierarchical Clustering
Abstract:

This project aims to study patterns in national responses to the pandemic and their effect on controlling the outbreak through a nonparametric density and regression technique and various clustering tools. A novel nonparametric density technique was utilized to consistently partition seven different countries’ COVID-19 cumulative growth curves into waves and phases, and each phase was modeled using linear regression. Every country’s model variables (the slope, r-square value, duration, and model type of each phase) were then connected to real-life factors, such as cluster outbreaks and government regulations. Multivariate correlation was conducted to uncover the relationships between model variables, and variable clustering showcased which model variable would be a good predictor for the infection situation in a certain 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.


Authors who are presenting talks have a * after their name.

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