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. 2012 Sep 6:12:99.
doi: 10.1186/1472-6947-12-99.

Developing open source, self-contained disease surveillance software applications for use in resource-limited settings

Timothy C Campbell  1 , Charles J Hodanics, Steven M Babin, Adjoa M Poku, Richard A Wojcik, Joseph F Skora, Jacqueline S Coberly, Zarna S Mistry, Sheri H Lewis
Affiliations

Developing open source, self-contained disease surveillance software applications for use in resource-limited settings

Timothy C Campbell et al. BMC Med Inform Decis Mak. .

Abstract

Background: Emerging public health threats often originate in resource-limited countries. In recognition of this fact, the World Health Organization issued revised International Health Regulations in 2005, which call for significantly increased reporting and response capabilities for all signatory nations. Electronic biosurveillance systems can improve the timeliness of public health data collection, aid in the early detection of and response to disease outbreaks, and enhance situational awareness.

Methods: As components of its Suite for Automated Global bioSurveillance (SAGES) program, The Johns Hopkins University Applied Physics Laboratory developed two open-source, electronic biosurveillance systems for use in resource-limited settings. OpenESSENCE provides web-based data entry, analysis, and reporting. ESSENCE Desktop Edition provides similar capabilities for settings without internet access. Both systems may be configured to collect data using locally available cell phone technologies.

Results: ESSENCE Desktop Edition has been deployed for two years in the Republic of the Philippines. Local health clinics have rapidly adopted the new technology to provide daily reporting, thus eliminating the two-to-three week data lag of the previous paper-based system.

Conclusions: OpenESSENCE and ESSENCE Desktop Edition are two open-source software products with the capability of significantly improving disease surveillance in a wide range of resource-limited settings. These products, and other emerging surveillance technologies, can assist resource-limited countries compliance with the revised International Health Regulations.

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Figures

Figure 1
Figure 1
User interface for entering data into OE.
Figure 2
Figure 2
Example of record format and data flow for cell phone inputs into OE.
Figure 3
Figure 3
Screen shot of the data report query form for OE. Note that the user can select which detector algorithm they wish to use.
Figure 4
Figure 4
OE screen shot illustrating configurable charting, pie/bar over configured groupings.
Figure 5
Figure 5
OE screen shot of time series analysis. Below the plot are details of the detection algorithm output. Note that detection algorithm alerts can be shown by the red peaks in the plot.
Figure 6
Figure 6
Screen shot of an example of data details analysis available in OE.
Figure 7
Figure 7
OE screen shot of geographic information system (GIS) map of data results.
Figure 8
Figure 8
Configuration wizard for adapting EDE data input to type of data.
Figure 9
Figure 9
Example of the user interface for creating a query in EDE.
Figure 10
Figure 10
EDE allows the user to examine the data in different slices, such as by sex or by diagnostic code type.
Figure 11
Figure 11
An example of a time series plot generated by EDE Note the red and yellow dots indicating different types of user-defined alerts.
Figure 12
Figure 12
EDE screen shot illustrating the results of multiple data queries and how comparisons may be made.
Figure 13
Figure 13
EDE can be used with Epi Info’s Epi Map for geographical display of data.
See this image and copyright information in PMC

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References

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