id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-208698-gm0b8u52	Fazeli, Shayan	Statistical Analytics and Regional Representation Learning for COVID-19 Pandemic Understanding	2020-08-08		.txt	text/plain	6364	325	49	• Evaluation of the informativeness of individual features in distinguishing between regions • Correlation analyses and investigating monotonic and non-monotonic relationships between several key features and the pandemic outcomes • Proposing a neural architecture for accurate short-term predictive modeling of the COVID-19 pandemic with minimal use of historical data by leveraging the automatically learned region representations Given the importance of open-research in dealing with the COVID-19 pandemic, we have also designed OLIVIA [5] . This work is distinguished from the mentioned projects and the majority of statistical works in this area in the sense that it is targeting the role of region-based features in the Spatio-temporal analysis of the pandemic with minimal use of historical data on the outbreak events. Our approach then used various statistical techniques and machine learning to measure the relationship between these regional representations and the pandemic time-series events and perform predictive modeling with minimal use of historical data on the epidemic.	./cache/cord-208698-gm0b8u52.txt	./txt/cord-208698-gm0b8u52.txt