Bayesian Statistics (SDS 390) Final Project, Fall 2020
Bayesian Analysis of Quality of Life and PPE (Personal Protective Equipment) Use During the Coronavirus (COVID-19) Pandemic Dianne Caravela, Natalia Iannucci, Hannah Snell, Elaine Ye
Abstract
Concerned with the documented psychological effects of the COVID-19 pandemic, this study aims to examine factors associated with quality of life and better understand PPE use during the pandemic. After controlling for demographic variables, (age, gender, education level, and chronic illnesses present) we created an association model for quality of life using Bayesian multiple regression with Markov Chain Monte Carlo; we also utilized Bayesian LASSO (techniques to create a model predicting PPE use. As hypothesized based on previous literature, annual income, being employed, being in long term relationships, and high mindfulness levels were all positively associated with quality of life scores during the pandemic; whereas self isolation was negatively related with psychological well-being. We found that the variables with the highest predictive power for PPE use were age, education, marital status, religion, intolerance of uncertainty, and avoidance of public settings. These results suggest that it is critical to keep in mind when planning pandemic responses that financial difficulties and social isolation have an impact on mental health. For individuals, our results stress the importance of maintaining social contact in a virtual or pandemic-safe way as well as the potential benefit of adding mindfulness practices to one’s daily routine. Additionally, understanding the key predictors of PPE usage can better drive education about PPE as well as allocation of PPE-related resources.
For Full Written Report, visit https://docs.google.com/document/d/1YgaUUq9KFQDTqYOvHgDlLgX4iheUAzkAeXitZz3veMk/edit?usp=sharing