id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-271027-4omocd8q	Fronza, R.	Spatial-temporal variations of atmospheric factors contribute to SARS-CoV-2 outbreak	2020-05-01		.txt	text/plain	5723	309	55	While it is possible to reason that observed variation in the number and severity of cases stem from the initial number of infected individuals, the difference in the testing policies and social aspects of community transmissions, the factors that could explain high discrepancy in areas with a similar level of healthcare still remain unknown. A generalized Poisson model was fitted to estimate the association among the data showing the number of infected cases per million and the atmospheric factors. Binary classifier based on an artificial neural network (ANN) was implemented to test the capacity of the atmospheric variables to predict the epidemic escalation of the number of positive cases per million on the basis of a combination of where l= PM2.5, PM10, NH 3 dM A l and O 3 . The expected number of infected cases in the total of 107 Italian provinces were predicted for the months of March (Spring), June (Summer), September (Autumn) and December (Winter) using the real measured values for PM2.5 and O 3 atmospheric factors from 2018 seasonal datasets.	./cache/cord-271027-4omocd8q.txt	./txt/cord-271027-4omocd8q.txt