Quantitative Analysis of Group for Epidemiology Architectural Approach

Abstract

AbstractEpidemiology, the aspect of research focusing on disease modelling is date intensive. Research epidemiologists in different research groups played a key role in developing different data driven model for COVID-19 and monkeypox. The requirement of accessing highly accurate data useful for disease modelling is beneficial but not without having challenges. Currently, the task of data acquisition is executed by select individuals in different research groups. This approach experiences the drawbacks associated with getting permission to access the desired data and inflexibility to change data acquisition goals due to dynamic epidemiological research objectives. The presented research addresses these challenges and proposes the design and use of dynamic intelligent crawlers for acquiring epidemiological data related to a given goal. In addition, the research aims to quantify how the use of computing entities enhances the process of data acquisition in epidemiological related studies. This is done by formulating and investigating the metrics of the data acquisition efficiency and the data analytics efficiency. The use of human assisted crawlers in the global information networks is found to enhance data acquisition efficiency (DAqE) and data analytics efficiency (DAnE). The use of human assisted crawlers in a hybrid configuration outperforms the case where manual research group member efforts are expended enhancing the DAqE and DAnE by up to 35% and 99% on average, respectively.