1. Corona Community Pitch: Maps Matter – Realistic Hot Spot Detection Across Regional Boundaries (Sebastian Cattes)

One standard visualisation of Covid-19 is a colored map showing the total infections per region. This does not inform about variations within and across regional boundaries and therefore diminishes the insights that could be derived from clustered data. This talk presents a novel kernel-heaping technique that generates a realistic picture of the spatial-temporal evolution of Covid-19. This approach allows identifying emerging infection hotspots at 30% higher accuracy than district-level data.

2. Corona Community Pitch: The Interplay of Demographic Variables and Social Distancing Scores in U.S. Covid-19 Cases (Francesca Tang)

With the severity of the COVID-19 outbreak, we characterize the nature of the growth trajectories of counties in the United States using a novel combination of spectral clustering and the correlation matrix. We capture the nature of counties’ growth in cases using growth communities, demographic factors, and social distancing performance to help government agencies utilize known information to make appropriate decisions regarding which potential counties to target resources and funding to.

3. Corona Community Pitch: The Process Perspective in Healthcare: Leveraging Process Mining for COVID-19 Event Data Analytics (Marco Pegoraro)

The recent increase in the availability of medical data, possible through automation and digitization of medical equipment, has enabled more accurate and complete analysis on patients’ medical data through many branches of data and process science. This talk illustrates some preliminary findings obtained with established process mining techniques in regard of the medical data of patients of the Uniklinik Aachen hospital affected by the recent epidemic of COVID-19.