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With the rapid continuing spread of COVID-19, it is clearly important to beable to track the progress of the virus over time in order to be betterprepared to anticipate its emergence and spread in new regions as well asdeclines in its presence in regions thereby leading to or justifying"reopening" decisions. There are many applications and web sites that monitorofficially released numbers of cases which are likely to be the most accuratemethods for tracking the progress of the virus; however, they will notnecessarily paint a complete picture. To begin filling any gaps in officialreports, we have developed the NewsStand CoronaViz web application(https://coronaviz.umiacs.io) that can run on desktops and mobile devices thatallows users to explore the geographic spread in discussions about the virusthrough analysis of keyword prevalence in geotagged news articles and tweets inrelation to the real spread of the virus as measured by confirmed case numbersreported by the appropriate authorities. NewsStand CoronaViz users have accessto dynamic variants of the disease-related variables corresponding to thenumbers of confirmed cases, active cases, deaths, and recoveries (where theyare provided) via a map query interface. It has the ability to step forward andbackward in time using both a variety of temporal window sizes (day, week,month, or combinations thereof) in addition to user-defined varying spatialwindow sizes specified by direct manipulation actions (e.g., pan, zoom, andhover) as well as textually (e.g., by the name of the containing country, stateor province, or county as well as textually-specified spatially-adjacentcombinations thereof), and finally by the amount of spatio-temporally-varyingnews and tweet volume involving COVID-19.

NewsStand CoronaViz: A Map Query Interface for Spatio-Temporal and Spatio-Textual Monitoring of Disease Spread