Previous research has noted that many factors greatly influence the spread of COVID-19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unobserved heterogeneity. Most contemporary COVID-19 related research has focused on modeling the relationship between explicitly measurable factors and the response variable of interest (such as the infection rate or the death rate). The infection rate is a commonly used metric for evaluating disease progression and a state's mitigation efforts. Because unobservable sources of heterogeneity cannot be measured directly, it is hard to incorporate them into the quantitative assessment and decision-making process. In this study, we propose new metrics to study a state's performance by adjusting the measurable county-level covariates and unobservable state-level heterogeneity through random effects. A hierarchical linear model (HLM) is postulated, and we calculate two model-based metrics-the standardized infection ratio (SDIR) and the adjusted infection rate (AIR). This analysis highlights certain time periods when the infection rate for a state was high while their SDIR was low and vice versa. We show that trends in these metrics can give insight into certain aspects of a state's performance. As each state continues to develop their individualized COVID-19 mitigation strategy and ultimately works to improve their performance, the SDIR and AIR may help supplement the crude infection rate metric to provide a more thorough understanding of a state's performance.
Keywords: COVID‐19; hierarchical linear models; infection rate; performance evaluation; random effects.
© 2021 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.