A case for location based contact tracing.

We present an evaluation of the effectiveness of manual contact tracing compared to bulletin board contact tracing. Classical contact tracing relies on reaching individuals who have been in proximity to an infectious person. A bulletin board approach focuses on identifying locations visited by an infectious person, and then contacting those who were at those locations. We present results comparing their effects on the overall reproductive number as well as the incidence and prevalence of disease. We evaluate them by building a new agent based simulation (ABS) model using the Susceptible Exposed Infectious and Recovered (SEIR) framework for disease spread. We conduct simulation experiments to quantify the effectiveness of these two models of contact tracing by calibrating the model to be compatible with SARS-CoV-2. We find that bulletin board contact tracing gives comparable results in terms of the reproductive number, duration, prevalence and incidence but is less resource intensive, easier to implement and offers a wider range of privacy options. Our experiments show that location-based bulletin board contact tracing can improve manual contact tracing.

Dynamic tables: an architecture for managing evolving, heterogeneous biomedical data in relational database management systems.

Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute-value-based database schemas to overcome limitations posed when using conventional relational database technology. This paper explores these issues and discusses why biomedical data are difficult to model using conventional relational techniques. The authors propose a solution to these obstacles based on a relational database engine using a sparse, column-store architecture. The authors provide benchmarks comparing the performance of queries and schema-modification operations using three different strategies: (1) the standard conventional relational design; (2) past approaches used by biomedical informatics researchers; and (3) their sparse, column-store architecture. The performance results show that their architecture is a promising technique for storing and processing many types of data that are not handled well by the other two semantic data models.