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arXiv · 2102.04260

Modeling and Forecasting of COVID-19 Spreading by Delayed Stochastic Differential Equations

Marouane Mahrouf·Adnane Boukhouima·Houssine Zine·El Mehdi Lotfi·Delfim F. M. Torres·Noura Yousfi
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Abstract

The novel coronavirus disease (COVID-19) pneumonia has posed a great threat to the world recent months by causing many deaths and enormous economic damage worldwide. The first case of COVID-19 in Morocco was reported on 2 March 2020, and the number of reported cases has increased day by day. In this work, we extend the well-known SIR compartmental model to deterministic and stochastic time-delayed models in order to predict the epidemiological trend of COVID-19 in Morocco and to assess the potential role of multiple preventive measures and strategies imposed by Moroccan authorities. The main features of the work include the well-posedness of the models and conditions under which the COVID-19 may become extinct or persist in the population. Parameter values have been estimated from real data and numerical simulations are presented for forecasting the COVID-19 spreading as well as verification of theoretical results.

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Marouane Mahrouf, Adnane Boukhouima, Houssine Zine, El Mehdi Lotfi, Delfim F. M. Torres, Noura Yousfi. 2021-02-04. Modeling and Forecasting of COVID-19 Spreading by Delayed Stochastic Differential Equations. https://doi.org/10.3390/axioms10010018

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