ABSTRACT
The high transmission rate of COVID-19 and the number of populations whose commute or recreational activities span across multiple counties/cities on a daily basis warrant serious attention to outbreaks in nearby areas beyond the administrative boundaries of counties/cities. We respond to these challenges by developing models using space–time cluster analysis to identify the hot spots and creating a dashboard that provides interactive visualizations of the results. The models use a Getis-Ord Gi statistic to identify clusters based on county-level daily confirmed cases, and Mann-Kendall tests combined with an adapted rule-based process to classify the type of the clusters. The space-time cluster analysis clearly displays the developing trends in the disease transmission patterns. The dashboard supports state- and county-level queries and expandable information. The dashboard displays the statistically tested spatiotemporal trends of COVID-19 breakout in concise and straightforward graphics.
