id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-279132-florvm7z	K., Branimir	From apparent to true – from frequency to distributions (II)	2020-08-17		.txt	text/plain	2390	113	45	According to Roda et al (2) , one of the main reasons for the variability in predicting the COVID-19 epidemic is the lack of data on the actual dynamics of the infection spread, which results in so-called nonidentifiability in model calibration. The authors determined the model parameters using the Bayesian approach and Markov chain Monte Carlo, and concluded that the COVID-19 epidemics in Wuhan and Seattle had likely been spreading for several weeks before they became apparent and were far more extensive than initially reported. Feroze (7) used Bayesian structural time series models to investigate the pattern of SARS-CoV-2 spread in India, Brazil, USA, Russia, and the UK between March 1 and June 29, 2020 to assess the impact of mitigation measures and predict the dynamics of the epidemic over the next 30 days. Dehning et al (9) used the SIR epidemiological model framework in combination with Bayesian inference to analyze the effective growth rate of the number of new cases over time.	./cache/cord-279132-florvm7z.txt	./txt/cord-279132-florvm7z.txt