Novel Data-Based Model for Future Epidemiology

Epidemiological studies aim at predicting the outbreak of diseases as epidemics and pandemics. This goal is often realized using closed form expressions that significantly utilize systems of differential equations. These systems of equations are often derived by groups of researchers working in global collaborative efforts. However, groups of researchers can experience a high workload in scenarios when there is large number of non–active participating researchers in a collaborative study. In addition, data explosion and increased availability can also undermine the efforts of hardworking researchers. This is because of the high velocity and variety associated with big data availability. Hence, an approach that helps researchers to address these challenges is required. The discussion in this research proposes a suitable solution in this regard. The proposed solution introduces the notion of cognitive epidemiology and epidemiological crawlers in a novel computing framework. In the proposed computing framework, crawlers and existing closed form expressions used in epidemiological studies interact. Prior to this interaction, epidemiological closed form expressions are formatted in a manner to enable the incorporation of intelligence capability. The research presents execution paths and discusses the incorporation alongside the integration of the proposed mechanism in a manner suitable for integration with the internet.