id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-319164-wbrnhpgs	Luellen, E.	A Machine Learning Explanation of Incidence Inequalities of SARS-CoV-2 Across 88 Days in 157 Countries	2020-06-08		.txt	text/plain	1911	114	49	Because the SARS-CoV-2 (COVID-19) pandemic viral outbreaks will likely continue until effective vaccines are widely administered, (1) new capabilities to accurately predict incidence rates by location and time to know in advance the disease burden and specific needs for any given population are valuable to minimize morbidity and mortality. In this study, a random forest of 9,250 regression trees was applied to 6,941 observations of 13 statistically significant predictor variables targeting SARS-CoV-2 incidence rates per 100,000 across 88 days in 157 countries. One key finding is an algorithm that can predict the incidence rate per day of a SARS-CoV-2 epidemic cycle with a pseudo-R2 accuracy of 98.5% and explain 97.4% of the variances. In this study, machine learning -a robust statistical version of artificial intelligence -was applied to a data set of 6,941 observations to identify the relative importance of 13 demographic, economic, environmental, and public health factors in modulating the incidence rate per 100,000 population of SARS-CoV-2 across 88 days in 157 countries.	./cache/cord-319164-wbrnhpgs.txt	./txt/cord-319164-wbrnhpgs.txt