Background- MERS- CoV is an airborne disease which spreads easily and has high death rate. To predict and prevent MERS-CoV, real time analysis of user’s health data and his/her geographical location is fundamental. Development of healthcare systems using cloud computing is emerging as an effective solution having benefits of better quality of service, reduced cost, scalability and flexibility.

Method- In this paper, an effective cloud computing system is proposed which predicts MERS-CoV infected patients using Bayesian belief network and provides geographical-based risk assessment to control its outbreak.

Results- Proposed system is tested on synthetic data generated for 0.2 million users. System provided high accuracy for classification and appropriate geographical-based risk assessment.

Conclusions- Key point of paper is the use of geographical positioning system to represent each MERS-CoV infected users on Google maps so that possibly-infected users can be quarantined as early as possible. It will help uninfected citizens to avoid regional exposure and the government agencies to manage the problem more effectively.

Full paper: MERS- Journal of Supercomputing