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

机译:延迟随机微分方程的Covid-19蔓延的建模与预测

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摘要

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.
机译:新型冠状病毒病(Covid-19)肺炎肺炎对世界最近几个月构成了巨大的威胁,使全世界许多死亡和巨大的经济损失。 2020年3月2日在摩洛哥报告了第一个Covid-19案例,报告案件的数量日益增加。在这项工作中,我们将众所周知的SIR分区模型扩展到确定性和随机时间延迟模型,以预测摩洛哥Covid-19的流行病学趋势,并评估多种预防措施和摩洛哥当局施加的策略的潜在作用。该工作的主要特征包括良好的模型和条件的良好良好,Covid-19可能灭绝或持续存在于人口。已经从真实数据估计了参数值,并且提出了数值模拟,以预测Covid-19扩展以及理论结果的验证。

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