A suitable data model for HIV infection and epidemic detection

Background: In recent years, there has been an increase in the amount and variety of data generated in the field of healthcare, (e.g., data related to the prevalence of contagious diseases in the society). Various patterns of individuals’ relationships in the society make the analysis of the network a complex, highly important process in detecting and preventing the incidence of diseases. Therefore, it would be helpful to propose a model for storing and processing related data which is especially designed for such an application. Materials and Methods: In this paper, a data model is proposed for the management of data for individuals infected with contagious diseases. This data model has the ability to efficiently detect the path of infectious diseases and the probable epidemicity. The proposed model is based on the graph data model, a type of NoSQL data model. In order to design this data model, essential requirements and queries were determined based on the needs of experts in this field. Results: The proposed data model was experimentally evaluated using Neo4j, a well-known graph data management system. It is shown in this paper that the proposed data model has a better performance than the traditional relational model in terms of system utilization and performance (i.e., data storage space, complexity and the time of finding the shortest infection path between two individuals, traversing the graph, finding at risk individuals, and etc.). Conclusions: The management of data for epidemic detection of HIV infection requires an appropriate data model that can provide the required functionalities and features with an acceptable quality. Graph data models are suitable NoSQL models for some of these features (e.g., epidemic detection via traversing of the graph). The proposed graph-based data model provides the main functionalities and features while outperforming performance and utilization metrics.