Web 2.0 Open-Intelligence Analysis for Disease and Pest Surveillance
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Abstract
Background
AquaticHealth.net is a web 2.0 open-source aquatic biosecurity intelligence gathering and analysis application. The system collects information in much the same way as other similar systems (e.g., HealthMap, BioCaster). However, the information collected undergoes minimal automated analysis, and analysis is largely left to AquaticHealth.net’s users. The result is an automated system of intelligence gathering, combined with a manual system of intelligence analysis. This approach relies on a large number of users, and so AquaticHealth.net relies on an open-intelligence analysis method: any user can publish their own analyses for all to see and analyse further. By combining automated data collection and human analysis, AquaticHealth.net will provide fast and accurate forecasts, accompanied with nuanced explanations. These methods can be applied to other areas of biosecurity and disease surveillance.
Objectives
To forecast aquatic biosecurity events using an open-intelligence platform.
Methods
Automated: AquaticHealth.net performs hourly scans of an array of RSS feeds, blogs, social networks, and news sites. It analyses this information and removes redundancies, applies taxonomy and geospatial tags. The information is then pushed to the Daily Scan, where users then analyse it further. Manual: Users asses the information for inaccuracies and its importance. They decide whether an article should be a disease alert, which is emailed to all users. Users can change tags, edit reports, add commentary, apply rankings, change searchterms, and summarise issues in the Emerging Issues blog (formerly a wiki).
Results
AquaticHealth.net has been online for 2 years and tends to publish 7 daily reports and 2 weekly disease alerts (on average). 90% of CEFAS’s (www.cefas.defra.gov.uk) Emerging Disease Updates cite AquaticHealth.net. The Australian Sub-Committee for Aquatic Animal Health (SCAAH) have used the system to compile their quarterly reports for 1.5 years. The Australian Department of Agriculture, Fisheries and Forestry (DAFF) have use AquaticHealth.net to make forecasts––e.g., used aquaculture equipment would be a high-risk pathway for OsHV---which have had an impact on Australian Quarantine Policy. Using the wiki-style Emerging Issues blog, the users of AquaticHealth.net have accurately forecasted several outbreaks (e.g., the recent outbreak of Amoebic Gill Disease in Norway). AquaticHealth.net’s users are currently forecasting an increase of emerging marine finfish disease outbeaks in South East Asia, and are actively watching this issue.
Conclusion
AquaticHealth.net’s web 2.0 open-intelligence approach has proven to be an effective and flexible biosecurity forecasting method. More work needs to be done in combining automated filters with social networks in order to harness the collective wisdom of any such system's users, but initial results are extremely promising.
AquaticHealth.net is a web 2.0 open-source aquatic biosecurity intelligence gathering and analysis application. The system collects information in much the same way as other similar systems (e.g., HealthMap, BioCaster). However, the information collected undergoes minimal automated analysis, and analysis is largely left to AquaticHealth.net’s users. The result is an automated system of intelligence gathering, combined with a manual system of intelligence analysis. This approach relies on a large number of users, and so AquaticHealth.net relies on an open-intelligence analysis method: any user can publish their own analyses for all to see and analyse further. By combining automated data collection and human analysis, AquaticHealth.net will provide fast and accurate forecasts, accompanied with nuanced explanations. These methods can be applied to other areas of biosecurity and disease surveillance.
Objectives
To forecast aquatic biosecurity events using an open-intelligence platform.
Methods
Automated: AquaticHealth.net performs hourly scans of an array of RSS feeds, blogs, social networks, and news sites. It analyses this information and removes redundancies, applies taxonomy and geospatial tags. The information is then pushed to the Daily Scan, where users then analyse it further. Manual: Users asses the information for inaccuracies and its importance. They decide whether an article should be a disease alert, which is emailed to all users. Users can change tags, edit reports, add commentary, apply rankings, change searchterms, and summarise issues in the Emerging Issues blog (formerly a wiki).
Results
AquaticHealth.net has been online for 2 years and tends to publish 7 daily reports and 2 weekly disease alerts (on average). 90% of CEFAS’s (www.cefas.defra.gov.uk) Emerging Disease Updates cite AquaticHealth.net. The Australian Sub-Committee for Aquatic Animal Health (SCAAH) have used the system to compile their quarterly reports for 1.5 years. The Australian Department of Agriculture, Fisheries and Forestry (DAFF) have use AquaticHealth.net to make forecasts––e.g., used aquaculture equipment would be a high-risk pathway for OsHV---which have had an impact on Australian Quarantine Policy. Using the wiki-style Emerging Issues blog, the users of AquaticHealth.net have accurately forecasted several outbreaks (e.g., the recent outbreak of Amoebic Gill Disease in Norway). AquaticHealth.net’s users are currently forecasting an increase of emerging marine finfish disease outbeaks in South East Asia, and are actively watching this issue.
Conclusion
AquaticHealth.net’s web 2.0 open-intelligence approach has proven to be an effective and flexible biosecurity forecasting method. More work needs to be done in combining automated filters with social networks in order to harness the collective wisdom of any such system's users, but initial results are extremely promising.
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