Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2007 Feb 14:7:4.
doi: 10.1186/1472-6947-7-4.

Using GIS to create synthetic disease outbreaks

Rochelle E Watkins  1 , Serryn Eagleson, Sam Beckett, Graeme Garner, Bert Veenendaal, Graeme Wright, Aileen J Plant
Affiliations

Using GIS to create synthetic disease outbreaks

Rochelle E Watkins et al. BMC Med Inform Decis Mak. .

Abstract

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Model state transitions.
Figure 2
Figure 2
Process diagram of main events and adjustable parameters within the simulation.
Figure 3
Figure 3
Start-up dialog.
Figure 4
Figure 4
Disease transmission parameters dialog.
Figure 5
Figure 5
Time series graph of simulated outbreak cases in the South-west region of Western Australia.
Figure 6
Figure 6
Map of simulated outbreak cases for the South-west region of Western Australia.
Figure 7
Figure 7
Time series graph of a simulated influenza outbreak in Western Australia.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Lawson AB, Klieinman K. Introduction: Spatial and syndromic surveillance for public health. In: Lawson AB and Klienman K, editor. Spatial and syndromic surveillance for public health. Chichester, Wiley; 2005. pp. 1–10.
    1. Buehler JW, Hopkins RS, Overhage JM, Sosin DM, Tong V, CDC working Group Framework for evaluating public health surveillance systems for early detection of outbreaks: Recommendations from the CDC Working Group. MMWR Recomm Rep. 2004;53:1–11. - PubMed
    1. Bonetti M, Pagano M. The interpoint distance distribution as a descriptor of point patterns, with an application to spatial disease clustering. Stat Med. 2005;24:753–773. - PubMed
    1. Hutwagner L, Browne T, Seeman GM, Fleischauer AT. Comparing aberration detection methods with simulated data. Emerg Infect Dis. 2005;11:314–316. - PMC - PubMed
    1. Kulldorff M, Zhang Z, Hartman J, Heffernan R, Huang L, Mostashari F. Evaluating disease outbreak detection methods: Benchmark data and power calculations. MMWR Morb Mortal Wkly Rep. 2004;53:144–151. - PubMed

Publication types

LinkOut - more resources