Infectious disease outbreaks provide a tremendous challenge for policymakers, who must implement control strategies under substantial time pressure and uncertainty. Mathematical models can be used to predict how control measures will work, and this can help policymakers make good decisions in the event of an epidemic. However, in the early stages of an outbreak, these models cannot do a very good job because they do not have enough accurate information about how the disease is spreading and how well control measures work. As a result, the suggestions made by these models are often added only when more reliable information becomes available.

We proposed statistical models to estimate the epidemiological characteristics at the early stage of both MERS-CoV and COVID-19. These estimates will help policymakers to set the quarantine period, resolve uncertainty, and adapt control measures as an outbreak progresses. We investigate in detail how the framework of non-pharmaceutical interventions can be applied to the control of epidemics in four regions of Saudi Arabia. We clarify the effects, benefits, and limitations of specific components, such as the difference between control measures taken by Saudi authorities at the level of regions to help minimize deaths and cases in Saudi Arabia, compared to what took place at the national level. We also cover a range of vaccine scenarios, such as vaccine prioritization, vaccine rollout, vaccine efficacy, and vaccine hesitancy. These approaches help guide decisions under uncertainty and provide clear, relevant information to decision-makers regarding the efficient allocation of control and monitoring resources. We believe the practical implementation of such an approach could greatly improve the outcome of epidemics in the future.