Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak.

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 Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (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. © The Author(s) under exclusive licence to Global Institute of Flexible Systems Management 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Citation

Jamal Al Qundus, Shivam Gupta, Hesham Abusaimeh, Silvio Peikert, Adrian Paschke. Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak. Global journal of flexible systems management. 2023;24(2):235-246

PMID: 37101929

search → result