Statistical Methods for Spatiotemporal Surveillance
DescriptionCurrently surveillance systems lack the ability to interrogate disparate data with diverse conditioned datasets from sources such as the Internet and hospital databases. The proposed research will develop effective spatiotemporal surveillance methods to enable early detection of disease outbreaks and accurate outbreak cluster identification based on multiple data streams related to infectious diseases. In public health surveillance, considerable attention has been paid to temporal and spatial surveillance methods. Temporal surveillance monitors event occurrences in a single region or location as time passes. Spatial surveillance focuses on data at a single time point and attempts to identify geographic clusters of high disease occurrences. Spatiotemporal surveillance refers to the simultaneous monitoring over time of event occurrences at multiple regions or locations. The objective is to accurately detect the time and location(s) of changes in the occurrence rate as soon as possible. In this research, we propose to develop new spatiotemporal surveillance methods that integrate and process multiple streams of infectious disease data.
|Effective start/end date||1/09/14 → 18/07/16|