id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-333162-gwmvsoru	Malki, Zohair	Association between Weather Data and COVID-19 Pandemic Predicting Mortality Rate: Machine Learning Approaches	2020-07-17		.txt	text/plain	942	63	54	title: Association between Weather Data and COVID-19 Pandemic Predicting Mortality Rate: Machine Learning Approaches In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. For Italy, regions 33 with a temperature higher than 15 degrees Celsius and 34 75% humidity have less spread of COVID-19 cases. Temperature and latitude 554 analysis to predict potential spread and seasonality for COVID-555 19 Temperature, population and longitu-571 dinal analysis to predict potential spread for COVID-19	./cache/cord-333162-gwmvsoru.txt	./txt/cord-333162-gwmvsoru.txt