Using GIS to create synthetic disease outbreaks

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Background. The ability to detect disease outbreaks in their early stages is a key component of efficient disease control and prevention. With the increased availability of electronic health-care data and spatio-temporal analysis techniques, there is great potential to develop algorithms to enable more effective disease surveillance. However, to ensure that the algorithms are effective they need to be evaluated. The objective of this research was to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data for outbreak detection algorithm evaluation. A state-transition model which simulates disease outbreaks in daily time steps using specified disease-specific parameters was developed to model the spread of infectious diseases transmitted by person-to-person contact. The software was developed using the MapBasic programming language for the MapInfo Professional geographic information system environment. Results. The simulation model developed is a generalised and flexible model which utilises the underlying distribution of the population and incorporates patterns of disease spread that can be customised to represent a range of infectious diseases and geographic locations. This model provides a means to explore the ability of outbreak detection algorithms to detect a variety of events across a large number of stochastic replications where the influence of uncertainty can be controlled. The software also allows historical data which is free from known outbreaks to be combined with simulated outbreak data to produce files for algorithm performance assessment. Conclusion. This simulation model provides a flexible method to generate data which may be useful for the evaluation and comparison of outbreak detection algorithm performance. © 2007 Watkins et al; licensee BioMed Central Ltd.




Watkins, R. E., Eagleson, S., Beckett, S., Garner, G., Veenendaal, B., Wright, G., & Plant, A. J. (2007). Using GIS to create synthetic disease outbreaks. BMC Medical Informatics and Decision Making, 7.

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