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New Metrics for Assessing the State Performance in Combating the COVID-19 Pandemic.
Li, Yun; Rice, Megan; Li, Moming; Du, Chengan; Xin, Xin; Wang, Zifu; Shi, Xun; Yang, Chaowei.
  • Li Y; Department of Geography and GeoInformation Science George Mason University Fairfax VA USA.
  • Rice M; NSF Spatiotemporal Innovation Center George Mason University Fairfax VA USA.
  • Li M; Department of Chemistry Carnegie Mellon University Pittsburgh PA USA.
  • Du C; Department of Epidemiology and Biostatistics University of California, San Francisco San Francisco CA USA.
  • Xin X; School of Internal Medicine Yale University New Heaven CT USA.
  • Wang Z; School of Internal Medicine Yale University New Heaven CT USA.
  • Shi X; Department of Geography and GeoInformation Science George Mason University Fairfax VA USA.
  • Yang C; NSF Spatiotemporal Innovation Center George Mason University Fairfax VA USA.
Geohealth ; 5(9): e2021GH000450, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1467050
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Geohealth Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Geohealth Year: 2021 Document Type: Article