A dynamic nonlinear autoregressive exogenous model for the prediction of COVID-19 ‎cases in ‎Jordan

Coronavirus (COVID-19) has captured the attention of globe very rapidly. Therefore, predicting spread disease become an indispensable process, this is being due to its extremely infectious nature and negative effects that some courses actions, which were taken minimize disease, have on economy key sectors (e.g., health, pharmaceutical industrial sectors). in research work, nonlinear autoregressive exogenous model (NARX) developed predict COVID-19 Jordan by mapping related factors (i.e. sociodemographic characteristics government actions) number confirmed cases twelve governorates Jordan. It been shown NARX can with a root mean square error approximately 28. The paper therefore lead efficient management available resources, help decision-makers selecting best course actions COVID-19.