A stochastic time-delayed model for the effectiveness of Moroccan COVID-19 deconfinement strategy

TL;DR

This paper develops a stochastic delayed mathematical model to analyze the impact of Morocco's COVID-19 deconfinement strategy, incorporating randomness and delays to predict disease spread and inform policy decisions.

Contribution

It introduces a novel stochastic delayed model for COVID-19 in Morocco, proving well-posedness and analyzing disease extinction conditions.

Findings

Model predicts COVID-19 trends post-deconfinement.

Stochastic effects influence disease extinction thresholds.

Numerical simulations evaluate deconfinement strategy effectiveness.

Abstract

Coronavirus disease 2019 (COVID-19) poses a great threat to public health and the economy worldwide. Currently, COVID-19 evolves in many countries to a second stage, characterized by the need for the liberation of the economy and relaxation of the human psychological effects. To this end, numerous countries decided to implement adequate deconfinement strategies. After the first prolongation of the established confinement, Morocco moves to the deconfinement stage on May 20, 2020. The relevant question concerns the impact on the COVID-19 propagation by considering an additional degree of realism related to stochastic noises due to the effectiveness level of the adapted measures. In this paper, we propose a delayed stochastic mathematical model to predict the epidemiological trend of COVID-19 in Morocco after the deconfinement. To ensure the well-posedness of the model, we prove the existence and uniqueness of a positive solution. Based on the large number theorem for martingales, we discuss the extinction of the disease under an appropriate threshold parameter. Moreover, numerical simulations are performed in order to test the efficiency of the deconfinement strategies chosen by the Moroccan authorities to help the policy makers and public health administration to make suitable decisions in the near future.