The current epidemic of coronavirus disease (COVID-19) has become a global crisis due to its rapid and wide spread contamination worldwide. A good understanding of disease dynamics would significantly improve the control and prevention of COVID19. The unique features of this pandemic have limited the applications of all existing models We used, for the Moroccan context, a Markov chain system and a SIR model for predicting the distribution of the disease. The same stochastic model was used in Mexico, it characterizes the probability distribution and the estimation of cases through a differential equation (modified SIR model). With this model, we will be able to characterize the disease and its evolution in order to be more prepared and to promote more actions on the part of policy makers than on the part of the general population.

An SIR model is an epidemiological mathematical modelused to model and predict the spread of infectious diseases(here we have as COVID-19 case), it divides the population to three compartments: S, I, R, (Susceptible, Infectious, or Recovered/Removed). Each member of the population typically progresses from susceptible to infectious to removed. This can be shown as a flow diagram in which the boxes represent the different compartments and the arrows the transition between compartments.

This chain consists of 12 states which correspond to the regions in the territory division of Morocco.

To sum up, our research was based on a Mexican research that predicts COVID-19 distribution in Mexico through a discrete and time-dependent Markov chain and an SIR-likemodel. We attempt to show stochastic model characterizing the probability distribution of cases in Morocco by regions and the estimated number of cases in Morocco through a differential equation model (modified SIR model), thus characterized the disease and its course in Morocco in order to display more preparedness and promote more logical actions by both the policy makers as well as the general population. By analyzing our models that we produces, the results were significant besides there is a small difference between predictions and reality of COVID19 evolution in Morocco due to Moroccan government decisions applied to reduce the spread of the epidemic that explains the importance of prediction researches in order to avoid very big loss in society.

• [1] Herman Scheepers, Markov Chain Analysis and Simulation usingPython, towardsdatascience Blog.
• [2] Alfonso Vivanco-Lira and Leon Guanajuato, Predicting COVID-19distribution in Mexico through a discrete and time-dependent Markovchain and an SIR-like model, Mexico.
• [3] https://www.bbc.com/news/world-africa-52801190.
• [4] https://ledesk.ma/datadesk/covid-19-le-maroc-evite-6-000-morts-le-picprevu-fin-avril-debut-mai-avec-800-deces-cumules
• [5] https://www.doctorsfornutrition.org/post/nutrition-in-the-time-of-covid-19
• [6] https://ourworldindata.org/coronavirus
• [7] https://infogram.com/evolution-du-covid-19-au-maroc-1h7j4drq7dlv2nr
• [8] https://scipython.com/book/chapter-8-scipy/additional-examples/the-sirepidemic-model
• [9] https://en.wikipedia.org/wiki/Compartmentalmodelsinepidemiology#TheSIRmodel
• [10] https://medium.com/@djconnel14663/seird-model-of-covid-19-596e6754c2c4