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https://www.selleckchem.com/products/ve-822.html The World Health Organization (WHO) declared in March 12, 2020 the COVID-19 disease as pandemic. In Morocco, the first local transmission case was detected in March 13. The number of confirmed cases has gradually increased to reach 15,194 on July 10, 2020. To predict the COVID-19 evolution, statistical and mathematical models such as generalized logistic growth model [1], exponential model [2], segmented Poisson model [3], Susceptible-Infected-Recovered derivative models [4] and ARIMA [5] have been proposed and used. Herein, we proposed the use of the Hidden Markov Chain, which is a statistical system modelling transitions from one state (confirmed cases, recovered, active or death) to another according to a transition probability matrix to forecast the evolution of COVID-19 in Morocco from March 14, to October 5, 2020. In our knowledge the Hidden Markov Chain was not yet applied to the COVID-19 spreading. Forecasts for the cumulative number of confirmed, recovered, active and death cases can help the Moroccan authorities to set up adequate protocols for managing the post-confinement due to COVID-19. We provided both the recorded and forecasted data matrices of the cumulative number of the confirmed, recovered and active cases through the range of the studied dates. We assessed how many peer-reviewed publications reporting chemical quantities and/or yields from electronic nicotine delivery systems (ENDS) have included adequate method validation characteristics in the publication for appropriate interpretation of data quality for informing tobacco regulatory science. We searched 5 databases (Web of Knowledge, PubMed, SciFinder, Embase, EBSCOhost) for ENDS publications between January 2007 and September 2018. Of the 283 publications screened, 173 publications were relevant for analysis. We identified the publications that report a certain degree of control in data quality, ie, the publications that report marginally val

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