A dynamic nonlinear autoregressive exogenous model for the prediction of COVID-19 ‎cases in ‎Jordan. Issue 1 (31st December 2022) - AlAlaween, Wafa' H.; Faouri, Noor M.; Al-Omar, Sarah H.; Hendaileh, Elias M.; Almousa, Aya Sama S.; AlAlawin, Abdallah H.; Abdallah, Omar H.; Albashabsheh, Nibal T.; Khorsheed, Bilal; Abueed, Omar A.
Abstract: Coronavirus (COVID-19) has captured the attention of the globe very rapidly. Therefore, predicting the spread of the disease has become an indispensable process, this is being due to its extremely infectious nature and due to the negative effects that some courses of actions, which were taken to minimize the spread of the disease, have on economy and key sectors (e.g., health, pharmaceutical and industrial sectors). Therefore, in this research work, the nonlinear autoregressive exogenous model (NARX) is developed to predict the spread of COVID-19 in Jordan by mapping the related factors (i.e. sociodemographic characteristics and government actions) to the number of confirmed COVID-19 cases in the twelve governorates in Jordan. It has been shown that the developed NARX model can predict the number of confirmed cases with a root mean square error of approximately 28. The NARX model developed in this paper can therefore lead to an efficient management of the available resources, and help decision-makers in selecting the best course of actions to minimize the spread of COVID-19.