Science Page: Swine Global Surveillance Project: update and future steps

This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.

This week, we are sharing an update on the Swine Global Surveillance Project, lead by the Center for Animal Health and Food Safety in collaboration with the UMN Veterinary Diagnostic Laboratory, the UMN swine group and the Swine Health Information Center (SHIC).

 Key Points:

  • It is a public, private and academic partnership to implement a system for near real time global surveillance of swine diseases.
  • The output of the system is a report of hazards identified and subsequently scored that may represent a risk for the US pork industry.
  • Developing systems to provide situational awareness to stakeholders in near-real time can facilitate the coordination between government agencies and the industry with the ultimate objective of preventing or mitigating the impact of diseases epidemics.
  • The reports are available at: https://z.umn.edu/SwineDiseaseSurveillance

The system of near real time global surveillance of swine diseases is based on an online application.  Initially focused on three main potential
threats: Classical Swine Fever (CSF), African Swine Fever (ASF), and Foot and Mouth Disease (FMD), it will expand to other exotic swine diseases in the US in the near future. A report, distributed on a monthly basis by SHIC, includes a list of identified hazards that may represent a risk for the US.

Swine global surveillance process steps

Several steps are needed to build the Swine Global Surveillance report as shown in the figure above.

  1. Screening/Filtering phase: Multiple official data sources and soft data sources are systematically screened to build a raw repository. After that, an Include/exclude process is undertaken under a crowdsourcing model.
  2. Scoring phase: A multi-criteria rubric was built based on: credibility, scale and speed of the outbreak, connectedness, local capacity to respond and potential financial impact on the US market. Each event is score independently by a group of experts.
  3. Quality assurance (QA)/building: Its aim being to ensure that the design, operation, and monitoring of processes/systems will comply with the principles of data integrity including control over intentional and unintentional changes to information. The monthly report is put into a PDF document automatically from the app after the scoring process is finalized. At last, assembly of figures and proofreading is done before sending it to SHIC for monthly publication.

Next steps

  • Complete automation of event capture into the database
  • Expansion of the list of diseases in the report
  • Shrinking the gap between Search/Filter phase and Final Publication – (1 week)
  • Expanding scoring experts panel
  • Process documentation – Quality assurance compliance

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