id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-318821-m8vx0wgs	Jombart, T.	Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection	2020-09-03		.txt	text/plain	6947	351	51	Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterise the main trend in an incidence. ASMODEE first identifies past temporal trends using automated model selection, and then uses outlier detection inspired by classical Shewhart control-charts to signal recent anomalous data points. COVID-19 incidence dynamics were simulated using a branching process model with realistic estimates of the time-varying reproduction number (Rt) and serial interval, under four scenarios: steady state (Rt close to 1), relapse, lockdown and flare-up following low levels of transmission. These values may need to be adjusted over time to ensure optimal detection of changes in temporal trends, and to balance the need for the calibration window to contain sufficient data points to fit the most complex time series model considered.	./cache/cord-318821-m8vx0wgs.txt	./txt/cord-318821-m8vx0wgs.txt