id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-340713-v5sdowb7	Bird, Jordan J.	Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach	2020-10-28		.txt	text/plain	5669	260	53	The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. The classification problem of risk is therefore formulated based on prior knowledge of the pandemic in terms of class only, but the attributes to attempt to classify them are purely country-level information regardless of number of cases, deaths and other coronavirus specific data. Country-level pandemic risk and preparedness classification based on COVID-19 data Fig 10 shows a comparison of other models that were explored. Country-level pandemic risk and preparedness classification based on COVID-19 data Table 1 shows the predicted class values for the best models applied to each of the respective risk classification problems.	./cache/cord-340713-v5sdowb7.txt	./txt/cord-340713-v5sdowb7.txt