Design and implementation of a Space-Time Intelligence System for disease surveillance

Abstract

Modeling chronic and infectious diseases entails tracking and describing individuals and their attributes (such as disease status, date of diagnosis, risk factors and so on) as they move and change through space and time. Using Geographic Information Systems, researchers can model, visualize and query spatial data, but their ability to address time has been limited by the lack of temporal referencing in the underlying data structures. In this paper, we discuss issues in designing data structures, indexing, and queries for spatio-temporal data within the context of health surveillance. We describe a space-time object model that treats modeled individuals as a chain of linked observations comprised of an ID, space-time coordinate, and time-referenced attributes. Movement models for these modeled individuals are functions that may be simple (e.g. linear, using vector representation) or more complex. We present several spatial, temporal, spatio-temporal and epidemiological queries emergent from the data model. We demonstrate this approach in a representative application, a simulation of the spread of influenza in a hospital ward.