key: cord-0901499-rp0hsbfa authors: Marfak, Abdelghafour; Achak, Doha; Azizi, Asmaa; Nejjari, Chakib; Aboudi, Khalid; Saad, Elmadani; Hilali, Abderraouf; Youlyouz-Marfak, Ibtissam title: The Hidden Markov Chain modelling of the COVID-19 spreading using Moroccan dataset date: 2020-07-24 journal: Data Brief DOI: 10.1016/j.dib.2020.106067 sha: 956930d8c3b56d2e62c392acde1dcd558e5cca24 doc_id: 901499 cord_uid: rp0hsbfa 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 15194 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. 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. COVID-19 spreading, Hidden Markov Chain, Statistical modelling, forecast. Table Subject Epidemiology Specific subject area Statistical model applied to the COVID-19 pandemic data to forecast the cumulative number of the confirmed, recovered, active and death cases Table Graph How data were acquired The data were acquired from the official website (https://covid19geomatic.hub.arcgis.com/) Instruments: The R package "markovchain" was used Data are in raw format and provided in an Excel file The data matrix consists of the reported cumulative number of the COVID-19 confirmed, recovered, active and death cases Data were obtained daily at 6 p.m. from the official report of health ministry for the pandemic situation. All data were collected between March 13 and July 10, 2020 yielding to a matrix data of 120 x 4 observations.  Basing on the predicted values up to October 5, 2020, the authorities can benefit from these data to set up adequate protocols for the post-confinement.  These data might be used by other researchers for comparison and further meta-analysis of the COVID-19 worldwide spreading.  These data may complete the statistical and mathematical models developed until now for modelling the COVID-19 evolution, which allow more understanding, modelling and managing epidemic crisis. Updates number of screening test for COVID-19 and the cumulative number of reported confirmed, recovered and death cases were obtained daily at 6 p.m. from the official report of health ministry for the pandemic situation [6, 7] . All data were collected between March 13 and July 10, 2020 yielding to a matrix data of 120 x 4 observations. In order to forecast the cumulative number of confirmed, recovered, active and death cases, our modeling of the COVID-19 spreading in Morocco started at March 13, 2020 using the following steps: 1) For any given day (j) we calculated: 2) The averages of the RC (A RC ), RR (A RR ), RD (A RD ) and RA (A RA ) rates were calculated from the 120 x 3) We considered a Markov process with the state space (healthy "H", active "A", recovered "R" and death "D"). The probabilities of transitioning were the averages' rates (A RC , A RR , A RD and A RA ) (Fig. 1A) . To forecast the cumulative number of confirmed, recovered, active and death cases, we used dynamic modelling. The "today (j) " state of the ("H", "A", "R", "D") system is multiplied iteratively by the transition matrix to predict the number of cases for "day (j+1) ". At each iteration, the estimated values for "day (j) " were used to forecast the number of cases on "day (j+1) " and so on. We started the process by fixing the initial condition to the first data point (March 13, 2020). We had 8 cumulative confirmed cases, 1 recovered and 1 death. The forecasts were estimated for March 14 to October 5, 2020. The RA and RR rates were assumed to obey a logistic distribution. While the RC and RD rates were simulated from exponential and normal distributions, respectively. We conducted 10,000 simulations and the 95% confidence intervals were estimated for each forecast of daily cumulative number of confirmed, recovered, active and death cases. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24 th Mathematic modeling of COVID-19 in the United States On a coupled time-dependent sir models fitting with New York and New-Jersey states COVID-19 data Application of the ARIMA model on the COVID-2019 epidemic dataset Moroccan Ministry of health GeoHub intelligent de suivi du COVID-19 au Maroc We would like to thank the National Center for Scientific and Technical Research (Rabat, Morocco) to have supported this work. The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.