Al Qundus Jamal, Gupta Shivam, Abusaimeh Hesham, Peikert Silvio, Paschke Adrian
Faculty of Information Technology, Middle East University, Amman, 11831 Jordan.
Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, 51100 Reims, France.
Glob J Flex Syst Manag. 2023;24(2):235-246. doi: 10.1007/s40171-023-00337-0. Epub 2023 Mar 17.
Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original (SIR) model to (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.
预测大流行病的爆发是一项重要措施，以帮助拯救受新冠疫情威胁的人们的生命。掌握有关大流行病可能传播的信息后，当局和民众就能做出更好的决策。例如，此类分析有助于制定更好的疫苗和药品分发策略。本文将原始的（SIR）模型修改为（SIRM）模型，该模型将免疫率作为一个参数，以加强对大流行病的预测。SIR是一种广泛用于预测大流行病传播的模型。许多类型的大流行病意味着SIR模型有许多变体，这使得很难找出与正在发生的大流行病相匹配的最佳模型。本文的模拟使用了已公布的有关大流行病传播的数据，以检验我们的新SIRM模型。结果清楚地表明，我们涵盖疫苗和药品方面的新SIRM模型是预测大流行病行为的合适模型。
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