Towards Data Driven Spatio-Temporal Threshold Identification Based on Cost Effective Public Health Information Management Framework

Appropriate public health action comes from data driven decision support systems. While sophisticated health information exchange framework may be costly in developing countries, the health care delivery system in place may provide a promising infrastructure that spans all parts in a region. Therefore, while digital and non digital data is constantly being generated from variety of sources including public and private health sectors, the health care delivery systems remain the primary and most fundamental source for data on population health status. For low and middle income countries with minimum digitization and resource constraints, traditional existing health care delivery system can be taken advantage of, for efficient production and timely transmission and utilisation of data to identify thresholds for disease outbreak in order to improve health status and health system performance. Due to lack of appropriate universally agreed criteria for threshold, defining local thresholds for infectious disease is not only crucial but also more appropriate. In this paper, we present a low cost data driven framework called Health Data Driven Framework (HDDF) through which data generated at health care facilities may be used for threshold detection and alarm generation. We also identify a localized method based on spatio-temporal mining of available data for appropriate threshold identification.