Abstract
Since the onset of the COVID-19 pandemic, researchers in the SIGSPATIAL community have utilized computational solutions to better explain, predict, and respond to infectious disease outbreaks. Using spatial computing for pandemic preparedness has also been highlighted as a major application of mobility data science [16]. At the beginning of the COVID-19 pandemic, the SIGSPATIAL community rapidly published ideas to improve our understanding of the spread of the virus in two SIGSPATIAL Special Newsletter Issues in March and July 2020 [28, 29]. These efforts led to the 1st and 2nd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology [5, 4] (formerly called Workshop on Modeling and Understanding the Spread of COVID-19 in 2020) which has provided authors of these newsletter articles a forum to present and discuss their solutions. Including both work published at the SIGSPATIAL Special Newsletter and regular peer-reviewed submissions, this workshop included topics such as the collection of large spatiotemporal datasets [20], leveraging data mining and spatial analysis techniques to analyze and visualize such data [2, 12, 25, 21, 11, 9, 8, 3], developing predictive spatial models and simulations [6, 1, 19, 13, 24, 10, 23, 14], and employing novel technologies towards contact tracing and surveillance [17, 26].
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