Event surveillance involves analyzing a region in order to detect patterns that are indicative of some event of interest. An example is the monitoring of information about emergency department visits to detect a disease outbreak. Spatial event surveillance involves analyzing spatial patterns of evidence that are indicative of the event of interest. A special case of spatial event surveillance is spatial cluster detection, which searches for subregions in which the count of an event of interest is higher than expected. Temporal event surveillance involves monitoring for emerging temporal patterns. Spatio-temporal event surveillance involves joint spatial and temporal monitoring.
When the events observed are of direct interest, then analyzing counts of those events is generally the preferred approach. However, in event surveillance we often only observe events that are indirectly related to the events of interest. For example, during an influenza outbreak, we may only have information about the chief complaints of patients who visited emergency departments. In this situation, a better surveillance approach may be to model the relationships among the events of interest and those observed.
I developed a high-level Bayesian network architecture that represents a class of spatial event surveillance models, which I call BayesNet-S. I also developed an architecture that represents a class of temporal event surveillance models called BayesNet-T. These Bayesian network architectures are combined into a single architecture that represents a class of spatio-temporal models called BayesNet-ST. Using these architectures, it is often possible to construct a temporal, spatial, or spatio-temporal model from an existing Bayesian network event-surveillance model that is non-spatial and non-temporal. My general hypothesis is that when an existing model is extended to incorporate space and time, event surveillance will be improved.
PANDA-CDCA (PC) (Cooper et al., 2007) is a non-temporal, non-spatial disease outbreak detection system. I extended PC both spatially and temporally. My specific hypothesis is that each of the spatial and temporal extensions of PC will perform outbreak detection better than does PC, and that the combined use of the spatial and temporal extensions will perform better than either extension alone.
The experimental results obtained in this research support this hypothesis.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-11102008-142102 |
| Date | 07 January 2009 |
| Creators | Jiang, Xia |
| Contributors | Daniel B. Neill, Wendy W. Chapman, Milos Hauskrecht, Gregory F. Cooper |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-11102008-142102/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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