This article has Open Peer Review reports available.
Developing open source, self-contained disease surveillance software applications for use in resource-limited settings
© Campbell et al.; licensee BioMed Central Ltd. 2012
Received: 22 May 2012
Accepted: 31 August 2012
Published: 6 September 2012
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.
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.
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.
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.
KeywordsElectronic biosurveillance Software development Public health Disease outbreak Resource-limited settings
Emerging public health threats often originate in countries that lack many public health resources and infrastructure . Because of the rapidity with which these diseases can spread, particularly with international air travel, early detection of disease outbreaks is extraordinarily important because it can provide for a quicker response and potentially limit morbidity, mortality, and the spread of the outbreak. In recognition of this fact, the World Health Organization issued revised International Health Regulations in 2005 (IHR 2005) that took effect in 2007. The purpose of IHR 2005 is to enhance global cooperation and protect populations from emerging health threats  by requiring participating countries to “develop core capacities for surveillance, detection, reporting and response.” These core capacities include legislation and financing, national and international communication, preparedness, human resources, and laboratory resources. The 195 member nations of the World Health Organization had until the summer of 2012 to comply with IHR 2005, which requires establishing capabilities for detecting, reporting, and assessing public health events involving a disease that would be a public health emergency of international concern. . These requirements may be challenging for resource-limited countries, but there are ways in which technology may help. In 2005, Fraser et al.  described a practical guide for implementing electronic medical record systems using open standards and open source software, based on pilot projects in six developing countries. Recently, Were et al.  described a scalable open source electronic health record (EHR) implementation model that relies upon a national technical expertise center for external support and maintains electronic health records at multiple sites in resource-limited settings. Dennehy et al.  described a partnership model for electronic health records in resource limited primary care settings. Ashar et al.  described a variety of information and communications technologies that can be used for electronic health data capture and assessed their use in resource-limited settings. In 2010, Hartley et al.  described the Global Health Security Initiative and discussed how electronic biosurveillance systems complement traditional public health surveillance to provide early warning and international awareness of disease outbreaks.
Syndromic surveillance systems typically use electronic, non-traditional, pre-diagnostic health indicators as surrogates for disease incidence to detect potential outbreaks in populations . These indicators may include a wide variety of data sources , such as over-the-counter and prescription drug sales data, emergency department visit chief complaint data, physician office visit insurance claims data, nurse hotline data, etc. Syndromic surveillance systems complement traditional public health surveillance by providing non-specific yet early pre-diagnostic indications of potential disease outbreaks . The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) is one example of an automated syndromic surveillance system. ESSENCE is a Java-based application used to monitor the health of populations and to detect disease outbreaks early and help prevent their spread . The fully-functional web-enabled version of ESSENCE (called Enterprise ESSENCE) is used by local and regional public health departments in different areas of the US and by the US Department of Defense and Veteran’s Administration . Enterprise ESSENCE is capable of collecting and analyzing a variety of data types and sources, and uses multiple anomaly detection algorithms to flag unusually high counts of disease indicators or alerts that are difficult for a human observer to see due to the volume and rate of change. System users can view, parse, plot, and map results, and share selected information with other users. Biosurveillance systems for resource-rich environments, such as Enterprise ESSENCE, are designed to use automated electronic data feeds and are best suited to areas with stable internet access.
Many global disease threats, like the 2003 Sudden Acute Respiratory Syndrome outbreak, first appear in resource-limited areas where electronic health data feeds and internet access are relatively unavailable or unreliable. Therefore, two additional versions of ESSENCE have been developed : ESSENCE Desktop Edition (EDE) and OpenESSENCE (OE). Unlike Enterprise ESSENCE which uses proprietary commercial products, EDE and OE utilize freely available open source software that provides several advantages in managing health data and performing medical surveillance in a variety of global communities. These advantages include wide scrutiny for quality assurance, low cost for acquisition and maintenance, and extensive user input on requirements, usage, and adaptability . Examples of the utility of open source software in health records and biosurveillance include: the public health data and information exchange methodology developed by the US Centers for Disease Control and Prevention (CDC) ; an open source electronic medical record for implementation in developing countries described by Mamlin et al. ; implementation of the Shibboleth information exchange for biosurveillance described by Lambert and Leonhardt ; and an open source cyber-environment especially for disease surveillance described by Edwards et al. .
Comparison of features of Enterprise ESSENCE, OpenESSENCE (OE), and ESSENCE Desktop Edition (EDE)
Uses proprietary 3rd party software
Deployable using only open source software
Supports multiple types of data
Supports a variety of data sources
Plug-in API* for detection algorithms
Security and encryption built into the core system
Customizable, dynamic, flexible design (property file driven)
Designed for internet access
Supports language internationalization
Supports font internationalization
Configurable security/access authentication
EDE is a desktop version of ESSENCE that was developed for resource-limited and disaster settings with little or no internet access. EDE runs on a stand-alone computer as a self-contained application designed to deploy easily and function in diverse settings. EDE supports open development and extensibility. Although it does not have a built-in data collection capability, EDE easily reads a variety of data file formats, and allows the user to rename and characterize input variables. In the Philippines and Asia, EDE data have been collected by personnel entering data directly into electronic clinic records and via simple short message service (SMS) text messaging. Other countries plan to use smart phone data forms to collect health data and transmit it via SMS.
As the name implies, OpenESSENCE (OE) is an open source application that includes key features of Enterprise ESSENCE and can be used either with the internet or as a stand-alone system [14, 21]. OE does not require the automatic secure internet data feeds that are used in Enterprise ESSENCE. Because of the distinct differences in Enterprise ESSENCE and its user community, converting all instances of Enterprise ESSENCE to OE is not planned at this time. Like EDE, OE provides for open development and extensibility, however, unlike EDE, it also contains a built-in data entry module. Data can be entered directly into the OE server, or via the web by multiple, geographically distributed users.
It should be emphasized that the primary goal of these efforts is to build capacity by giving resource-limited countries the independent ability to collect and analyze their own data. Because of this, JHU/APL does not have access to their data nor is that a goal. Based on more than a decade of experience working with local public health officials within the US, JHU/APL interacts closely with local public health officials in these countries in order to address quickly their concerns about best utilizing and maintaining their new system. The emphasis is on rapidly providing value as recognized by the user so that they begin using these systems as much as possible.
OE and EDE were developed to support a wide variety of user needs ‘out of the box,’ including configuration for different data sources and for user-performed queries on that data. EDE can be downloaded from the web and loaded onto most Windows-based laptop or desktop computers. Development is not yet complete on OE, so for the moment it requires active installation by JHU/APL developers. Eventually, we intend to offer the OE version as a downloadable product. The biosurveillance system results are provided in a format similar to Enterprise ESSENCE [12, 13] and include graphs, charts, and detailed data on case reports, as well as geographic maps of individual illness reports.
Overview of software framework
Security, encryption, and user-interface language internationalization support are built into the core OE system. OE uses open source software and industry standard technologies (e.g., Java EE, Apache Tomcat, Spring, MySQL, PostgreSQL, PostGIS, GeoServer, GeoExt, Openlayers). The overall design is modular with component-based applications and a data-driven system structure. OE utilizes a Java development open source framework called Spring [22, 23]. Spring contains modular features for developing web applications in Java. These features include aspect-oriented programming, transaction application programming interface (API), configurable security processes for authentication and authorization, the use of XML schema, and data access for working with relational databases. OE also uses Groovy [24, 25], an object-oriented programming language that enhances and interoperates with the features of Java. Groovy is used to implement the interface layer for the data sources, allowing dynamic table and field definitions. Spring provides comprehensive support for using classes and objects defined by Groovy. This OE design allows for dynamic extension and reconfiguration, and eliminates the need to re-develop or rebuild an application in order to incorporate adjustments or enhancements. Plug-in APIs are used for the detection algorithms. Various libraries and server applications are stable.
Initial user interface and data input
In many resource-limited countries, cellular telephone usage is already or is rapidly becoming very common . These services often include the ability to transmit text messages or SMS. Therefore, SMS messages can be used as a way of collecting health data and entering it into an electronic surveillance system [7, 28]. JHU/APL is piloting such systems in Asia and Central America.
Analysis and visualization
Tools to analyze and visualize data and derived data products are important ways in which biosurveillance systems can enhance public health decision making. It is important to note that OE provides the ability for multiple users to share information derived from data without having to share sensitive private health data. Such ability enhances the sharing of information across jurisdictional boundaries.
ESSENCE desktop edition (EDE)
Overview of software framework
desktop core, which provides the main user interfaces;
desktop data core, which provides the underlying data query mechanism;
detector temporal core, which provides detection algorithm interfaces.
User interface and data input
Analysis and visualization
In addition, EDE supports the Desktop Internet Geographic Information System (uDig) mapping application, which is an open source Java-based geospatial data viewer/editor toolkit  built with Eclipse RCP. As with EpiMap, uDig can be launched for a specific query and the map appearance customized by the user. uDig also allows developers to customize and integrate geographic information systems (GIS) applications, including ESRI shape files, PostGIS, and geospatial web services.
The OE system was only recently deployed in 2011 and results are not yet available, but the EDE system has been in use long enough for preliminary results to be presented. The initial deployment of EDE was as an add-in module attached to the national disease surveillance system in the Philippines called the Philippines Integrated Disease Surveillance and Response (PIDSR) program. The goal of PIDSR is to reduce morbidity and mortality through a nationwide system that integrates facility-based information systems. EDE is now integrated with PIDSR and is used to monitor the temporal trends of diseases that are officially notifiable in the Philippines. Epidemiologists and computer scientists from JHU/APL visited the Philippines to solicit input from the stakeholders about the system architecture, types of usage, means of inputting data, and training. In 2009, a pilot study was begun to evaluate a simple fever surveillance protocol using SMS text messages to send daily, patient-level data from several local health clinics to the city health office in Cebu City . These data were included in a single SMS text message for each patient who presented with fever at the local clinics. Family and address codes, age, sex, date of onset, and presenting signs and symptoms were recorded for each patient as per the usual protocol. A formatted SMS text message about each eligible patient was sent each day to a receiver phone connected to a computer at the city health office. Standardized abbreviations were adopted for specific signs and symptoms (e.g., ha = headache). An SQL application was used to download the SMS data from the phone to an Epi Info database, which was analyzed using EDE by the city epidemiologist. Recently, the local health department has expanded SMS data inputs to all of Cebu City.
Before implementation of the SMS system, there had been a minimum two weeks delay between case presentation and case reporting to the city health office. SMS text messaging was the only practical alternative for this particular location because none of the local health clinics had an operable computer or internet connection. SMS was inexpensive and commonly used in this region. Using SMS also allowed the currently used logbook format to be maintained. Within a month of implementation of fever case data collection, 30 % of the local health clinics were using SMS texting to send daily fever case reports to the city health office. Within two months of implementation, this usage expanded to 90 % of the local health clinics and so far remains at about that level. The primary obstacle to increasing usage has been that local health department personnel have been accustomed to monitoring data post-event or at weekly or longer intervals based on previous data availability. Now that they have the ability to do daily monitoring, it can take some time for the users to become accustomed to doing so.
Discussion and conclusions
Based on information obtained from biosurveillance stakeholders in resource-limited countries, JHU/APL learned the following lessons: 1) the ability to maintain control of one’s own data is very important to the users, 2) open-source software is particularly desirable, 3) the system should place a minimum burden on those providing data, 4) system acceptance results from enabling the user to easily tailor the system to local needs, and 5) sustainability results from local ownership and working within existing needs and capabilities. This list of points is based upon informal qualitative discussions with local users regarding their needs, as our primary focus has been on rapid technology insertion, refinement, and increasing usage capacity. In addition to these lessons, our experience has revealed the importance of interaction with the appropriate levels of the local and national governments and the identification of key individuals who can serve as champions of the project, including those involved in local policy and financial matters as well as the actual users.
Therefore, the OE and EDE systems were created by JHU/APL to offer self-contained disease surveillance tools that can be deployed efficiently at a variety of resource-limited locations, as well as disaster locations. Both these systems are easily upgradeable and extendable. While skilled information technology professionals may be difficult for public health departments to find and retain, the OE and EDE systems have been simple enough to operate that this has not been a significant limitation. EDE was designed as a stand-alone desktop application, and OE can be used as a desktop or web-based application. Each provides similar functionality to the current web deployment of Enterprise ESSENCE . Both systems are based on a modular, component-based application design. This design allows for improved testability of components and isolation of problems. Repurposing and reuse is also easier and more likely with a modular design because it mitigates the need to redevelop or rebuild an application to incorporate adjustments or enhancements. The system structure is data driven to allow for dynamic extension and reconfiguration.
These biosurveillance systems were developed to support a wide variety of user needs in different settings. Both systems will provide user-defined preferences and mechanisms for data input from several types of databases. Users can configure the system specifically for the variables included in their database, thereby easing common data ingestion problems, especially the difficulties in trying to get disparate data formats to fit a specific type of data ingestion. Users can design their own queries and use different detection algorithms to analyze their data, including temporal and spatial analysis.
EDE utilizes an Eclipse RCP framework, while OE uses the Spring framework and Groovy Java language extensions. While both systems can operate on a single computer without a network connection, the OE system is designed to utilize the benefits of network connectivity. The OE system can also be used to share actionable information via the internet among multiple users and across different jurisdictions. Both systems provide results in a format similar to Enterprise ESSENCE , including graphs, charts, detailed data on individual illness reports, and geographic maps of locations of individual illness reports. As open source stand-alone desktop applications, they are easily deployable, upgradeable and extendable. These features make EDE and OE ideal for rapid deployment in resource-limited environments where the infrastructure for a full-scale web-based biosurveillance system is not immediately feasible. Future efforts are to continue to improve the software features available to the user, while keeping the interfaces and components as simple as possible so that maintenance and sustainability do not require someone with a high level of information technology expertise. Such biosurveillance systems may then improve the timeliness of data collection and enhance the early detection of disease outbreaks, thereby allowing time for mitigation of the effects of these outbreaks.
The views expressed here are the opinions of the authors and are not to be construed as official or as representing the views of the US Department of the Navy or the US Department of Defense.
We would like to acknowledge the collaboration with and financial support of the U.S. Armed Forces Health Surveillance Center, Division of GEIS Operations and the invaluable assistance from our colleagues, including: CDR David L. Blazes, AFHSC-GEIS; Dr. InKyu Yoon from the US Armed Forces Research Institute of Medical Sciences in Bangkok, Thailand; Dr. John Mark S. Velasco, Ms. Agnes D. Tomayano, Mr. Danny C. Obidas, Mr. Jewernest C. Casquejo, and Dr. Maria Theresa P. Alera from the US Armed Forces Research Institute of Medical Sciences Virology Research Unit, Republic of the Philippines; Dr. Fe A. Cabugao, Dr. Ilya A. Tac-an, and Ms. Durinda R. Macasocol from the Cebu City Health Office, Republic of the Philippines; and Dr. Joan M. Neyra, Dr. Delphis M. Vera, Dr. Ricardo A. Hora, and Dr. Joel Montgomery at the US Naval Medical Research Center Detachment in Lima, Peru. We thank Ms. Shradda Patel and Dr. Brian Feighner for their assistance on this manuscript. We are deeply indebted to Mr. Wayne A. Loschen, the developer of the Enterprise ESSENCE system, for his sage guidance on these projects.
- Jones K, Patel N, Levy M, Storeygard A, Balk D, Gittleman J, Daszak P: Global trends in emerging infectious diseases. Nature. 2007, 451: 990-993. 10.1038/nature06536.View ArticleGoogle Scholar
- Katz RL, Gornto SE, Chretien JP: Expert opinion on implementation strategies for the International Health Regulations (2005). World Med Health Policy. 2011, 3 (3): 10.2202/1948-4682.1178.Google Scholar
- World Health Organization (WHO): International Health Regulations. 2005, http://whqlibdoc.who.int/publications/2008/9789241580410_eng.pdf Accessed 25 April 2012, 2Google Scholar
- Fraser H, Biondich P, Moodley D, Choi S, Mamlin B, Szolovits P: Implementing electronic medical record systems in developing countries. Inform Prim Care. 2005, 13: 83-95.PubMedGoogle Scholar
- Were M, Emenyonu N, Achieng M, Shen C, Ssali J, Masaba J, Tierney W: Evaluating a scalable model for implementing electronic health records in resource-limited settings. J Am Med Inform Assoc. 2010, 17: 237-244.View ArticlePubMedPubMed CentralGoogle Scholar
- Dennehy P, White M, Hamilton A, Pohl J, Tanner C, Onifade T, Zheng K: A partnership model for implementing electronic health records in resource-limited primary care settings: experiences from two nurse-managed health centers. J Am Med Inform Assoc. 2011, 18: 820-826. 10.1136/amiajnl-2011-000117.View ArticlePubMedPubMed CentralGoogle Scholar
- Ashar R, Lewis S, Blazes D, Chretien J: Applying information and communications technologies to collect health data from remote settings: a systematic assessment of current technologies. J Biomed Inform. 2010, 43: 332-341. 10.1016/j.jbi.2009.11.009.View ArticlePubMedGoogle Scholar
- Hartley DM, Nelson N, Walters R, Arthur R, Yangarber R, Madoff L, Linge J, Mawudeku A, Collier N, Brownstein J, Thinus G, Lightfoot N: Landscape of international event-based biosurveillance. Emerg Health Threats. 2010, 3: e3-10.3134/ehtj.10.003.Google Scholar
- Chretien JP, Burkom H, Sedyaningsih ER, Lasratit RP, Lescano AG, Mundaca CC, Blazes DL, Munayco CV, Coberly JS, Ashar RJ, Lewis SH: Syndromic surveillance: adapting innovations to developing settings. PLoS Med. 2008, 5 (3): e72-10.1371/journal.pmed.0050072.View ArticlePubMedPubMed CentralGoogle Scholar
- Babin S, Magruder S, Hakre S, Coberly J, Lombardo J: Understanding the data: health indicators in disease surveillance. Disease Surveillance: A Public Health Informatics Approach. Edited by: Lombardo J, Buckeridge D. 2007, John Wiley and Sons, Hoboken, New Jersey, 43-90.Google Scholar
- Lazarus R, Kleinman K, Dashevsky I, DeMaria A, Platt R: Using automated medical records for rapid identification of illness syndromes (syndromic surveillance): the example of lower respiratory infection. BMC Publ Health. 2001, 1: 9-10.1186/1471-2458-1-9.View ArticleGoogle Scholar
- Lombardo J, Burkom H, Elbert E, Magruder S, Happel Lewis S, Loschen W, Sari J, Sniegoski C, Wojcik R, Pavlin J: A systems overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II). J Urban Health. 2003, 80 (2, Supplement 1): i31-i41.Google Scholar
- Lucero C, Oda G, Cox K, Maldonado F, Lombardo J, Wojcik R, Holodny M: Enhanced health event detection and influenza surveillance using a joint Veterans Affairs and Department of Defense biosurveillance system. BMC Med Inform Decis Mak. 2011, 11: 56-10.1186/1472-6947-11-56.View ArticlePubMedPubMed CentralGoogle Scholar
- Lewis S, Feighner B, Loschen W, Wojcik R, Skora J, Coberly J, Blazes D: SAGES: A suite of freely available software tools for electronic disease surveillance in resource-limited settings. PLoS One. 2011, 6 (5): e19750-10.1371/journal.pone.0019750.View ArticlePubMedPubMed CentralGoogle Scholar
- Kalra D, Forslund D: Open source health systems. Person-Centered Health Records. Edited by: Demetriades J, Kolodner R, Chrsitopherson G. 2005, Springer, New York, 169-185.View ArticleGoogle Scholar
- Savel T, Hall K, Lee B, McMullin V, Miles M, Stinn J, White P, Washington D, Boyd T, Lenert L: A Public Health Grid (PHGrid): architecture and value proposition for 21st century public health. Int J Med Inform. 2010, 79: 523-529. 10.1016/j.ijmedinf.2010.04.002.View ArticlePubMedGoogle Scholar
- Mamlin BW, Biondich P, Wolfe B, Fraser H, Jazayeri D, Allen C, Miranda J, Tierney W: Cooking up an open-source EMR for developing countries: OpenMRS – A recipe for successful collaboration. Proc AMIA Annu Symp. 2006, 2006: Spp529-533.Google Scholar
- Lambert HD, Leonhardt CF: Federated authentication to support information sharing: Shibboleth in a bio-surveillance information grid. Int Congr Ser. 2004, 1268: 135-150.View ArticleGoogle Scholar
- Edwards WA, Vaid A, Brooks IS: INDICATOR: an open-source cyberenvironment for biosurveillance. 2010, Proc. 7th International ISCRAM Conference, Seattle, WA, USA, MayGoogle Scholar
- Johns M, Blazes D: International Health Regulations (2005) and the US Department of Defense: building core capacities on a foundation of partnership and trust. BMC Publ Health. 2010, 10 (Suppl 1): 54-View ArticleGoogle Scholar
- Lewis S, Feighner B, Campbell T, Wojcik R, Coberly J, Blazes D: Suite for automated global electronic biosurveillance (SAGES). 2011, US Centers for Disease Control and Prevention 2011 Public Health Informatics Conference, Session on Low Cost Tools for International Success, Atlanta, GA, USA, August 22Google Scholar
- Johnson R, Hoeller J, Arendsen A, Risberg T: Professional Java Development with the Spring Framework. 2005, Wrox Press, division of John Wiley and Sons, Hoboken, NJ, 672-Google Scholar
- Spring software: SpringSource, a division of VMware. 2011, http://www.springsource.org. Accessed 2 DecemberGoogle Scholar
- Koenig D, Glover A, King P, Laforge G, Skeet J, Gosling J: Groovy in Action. 2007, Manning Publications, Shelter Island, NY, 696-Google Scholar
- Groovy software: SpringSource, a division of VMware. 2011, http://groovy.codehaus.org. Accessed 2 DecemberGoogle Scholar
- Lewis S, Wojcik R: Methodologies for data collection. BMC Proc. 2008, 2 (Suppl 3): S5-10.1186/1753-6561-2-s3-s5.View ArticlePubMedPubMed CentralGoogle Scholar
- The power of mobile money: The power of mobile money. The Economist. 2009, http://www.economist.com/14505519/]. Accessed 30 November 2011Google Scholar
- Coberly J, Tomayo A, Velasco JM, Cabugao F, Tac-an I, Wojcik R: Dengue SMS surveillance project in the Philippines. US CDC Public Health Information Network Conference. 2009, US Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA, September 1Google Scholar
- Kulldorff M: Prospective time periodic geographical disease surveillance using a scan statistic. J R Stat Soc Ser A. 2001, 164 (1): 61-72. 10.1111/1467-985X.00186.View ArticleGoogle Scholar
- Kulldorf M: SaTScan software. 2011, Harvard Medical School, Boston, MA 02215, USA, http://www.satscan.org, Accessed 9 DecemberGoogle Scholar
- Burkom H: Biosurveillance applying scan statistics with multiple, disparate data sources. J Urban Health. 2003, 80 (2 Suppl 1): i57-i65.PubMedPubMed CentralGoogle Scholar
- McAffer J, Lemieux J-M: Eclipse Rich Client Platform: Designing, Coding, and Packaging Java Applications. 2006, Addison-Wesley Professional Publishers, division of Pearson, London, 504-Google Scholar
- Epi Info software: US Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA 30333 USA: http://wwwn.cdc.gov/epiinfo, Accessed 22 November 2011
- uDig software: Refractions Research, Suite 300, 1207 Douglas Street, Victoria, BC, V8W 2E7, Canada: http://udig.refractions.net, Accessed 22 November 2011
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/12/99/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.