Using Mathematical and Statistical Model to Forecast the Path of Infection by Covid-19 in the Kingdom of Saudi Arabia

Abstract

Saudi Arabia, like any other part of the earthly globe, has been exposed to the Covid-19 pandemic The first case appeared on March 3, 2020, followed by an increase in the number of infections until it reached thousands with the numbers on the rise Therefore, adopting clear strategies to deal with the pandemic according to specific data on its size is necessary In this study, the time series of the number of infections and deaths were analyzed to study the behavior of the pandemic over time The cumulative curve of the phenomenon was analyzed to show the extent of the pandemic s decline or spread On the other hand, the time curve of the number of cases of the pandemic was fitted based on a set of mathematical and statistical models, which were divided into three sections [nonlinear growth model, Susceptible, Exposed, Infectious, Recovered (SEIR) model, regression model] to attain the best possible fitting of the relationship curve The results show that the Weibull model and Polynomial model at (n = 4) are the best models for fitting the relationship at short run and the SEIR model gives better relationship fitting at long run In conclusion, there is a tendency for the disease to decline during the short period, while expecting other waves of the epidemic that will recede in the long term with the emergence of a suitable vaccine [ABSTRACT FROM AUTHOR] Copyright of IIUM Medical Journal Malaysia is the property of International Islamic University Malaysia, Faculty of Medicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder s express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )