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arXiv · 2003.00107

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

John Kastner·Hanan Samet·Hong Wei
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Abstract

With the rapid continuing spread of COVID-19, it is clearly important to be able to track the progress of the virus over time in order to be better prepared to anticipate its emergence and spread in new regions as well as declines in its presence in regions thereby leading to or justifying "reopening" decisions. There are many applications and web sites that monitor officially released numbers of cases which are likely to be the most accurate methods for tracking the progress of the virus; however, they will not necessarily paint a complete picture. To begin filling any gaps in official reports, we have developed the NewsStand CoronaViz web application (https://coronaviz.umiacs.io) that can run on desktops and mobile devices that allows users to explore the geographic spread in discussions about the virus through analysis of keyword prevalence in geotagged news articles and tweets in relation to the real spread of the virus as measured by confirmed case numbers reported by the appropriate authorities. NewsStand CoronaViz users have access to dynamic variants of the disease-related variables corresponding to the numbers of confirmed cases, active cases, deaths, and recoveries (where they are provided) via a map query interface. It has the ability to step forward and backward in time using both a variety of temporal window sizes (day, week, month, or combinations thereof) in addition to user-defined varying spatial window sizes specified by direct manipulation actions (e.g., pan, zoom, and hover) as well as textually (e.g., by the name of the containing country, state or province, or county as well as textually-specified spatially-adjacent combinations thereof), and finally by the amount of spatio-temporally-varying news and tweet volume involving COVID-19.

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John Kastner, Hanan Samet, Hong Wei. 2020-02-28. NewsStand CoronaViz: A Map Query Interface for Spatio-Temporal and Spatio-Textual Monitoring of Disease Spread. https://arxiv.org/abs/2003.00107

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