Duration: 08/01/2007 to 07/01/2009

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

Regional Animal Health Situational Awareness Project
A Multi-State Hatch Research Proposal
May, 2007 v1.0

Craig Carter, Professor, Livestock Disease Diagnostic Center, University of Kentucky
Agricola Odoi, Assistant Professor, College of Veterinary Medicine, University of Tennessee
Jackie Smith, Research Analyst, Livestock Disease Diagnostic Center, University of Kentucky

The Need

The marketability and value of food animal products is directly related to the level of success that farmers, veterinarians, state and federal officials have in maintaining the health of their herds. Currently, farmers raising food animals are totally reliant on their ability to recognize health problems in their own animals and to call in veterinarians for assistance with clinical problems as needed. In turn, practicing veterinarians utilize veterinary diagnostic laboratories to help in obtaining a diagnosis on clinical cases seen in the field. Unfortunately, these individual animal health events and diagnoses are not shared with other farms in the same locale. Therefore, farmers do not reap the benefits of medical situational awareness which could help them to anticipate impending animal health risks and to take preventive action (e.g. vaccination, prophylaxis) in their own herds.

Regionalized collection and analysis of animal health events would provide a mechanism for improving medical situational awareness in a focused locale, state or collection of states. The purpose of this project is to build an animal health medical awareness system for the states of Kentucky and Tennessee which will demonstrate proof-of-concept that a multi-state network of this kind can economically benefit animal agriculture in the U.S.

Importance of the Work

Nearly 16% of the gross domestic product of the U.S. is generated by agriculture ($1.5 trillion annually). The total gross income from all cattle in the U.S. for 2006 was $64.2 billion. For 2005, the total value of Kentucky agricultural animal products was estimated at $2.8 billion dollars. Kentucky cattle gross receipts totaled $450 million during the same year with Tennessee not far behind at $361 million. Exports of U.S. beef alone have almost quintupled since the mid-seventies. During this same timeframe, our animal healthcare framework has not changed in fundamental ways. This has resulted in U.S. animal agriculture tacitly accepting a fixed level of endemic disease and economic losses.

One study suggests that 3-4% of the value of animal production in agriculture is lost to animal diseases every year. This calculates out to over $100 million in estimated losses to Kentucky animal agriculture annually. Improved medical situational awareness in and around farms and at a regional level opens up the potential for early identification of risk factors and transmission of animal disease, leading to an earlier medical response. Early medical responses have historically been proven to lead to a better health outcome. Assuming this awareness led to only a 1% decrease in the overall economic impact of animal disease, the result would be a savings of over $6B in the U.S. and $81M in the states of Kentucky and Tennessee alone.

Technical Feasibility of the Work

Funded by a grant from the Department of Homeland Security from 2006-2007, the University of Kentucky Livestock Disease Diagnostic Center and the College of Veterinary Medicine at the University of Tennessee have developed a syndromic and laboratory surveillance system for the early identification of animal diseases. The system incorporates a scan statistic that can examine health events over time and space and identifies statistical clusters of events as soon as they occur. Currently, the primary data streams being processed by the system are diagnostic laboratory test results including animal necropsies (deaths), thereby providing a near-real-time awareness and alerting to key animal health staff (e.g. State Veterinarian, veterinary epidemiologists). These alerts stimulate responses to the events which can lead to the mitigation and/or prevention of animal diseases.

The principal investigators of this project believe that this tool can be expanded and generalized to process more data streams that are linked to animal health (e.g. farm-level syndromic data, toxic plants, rainfall, vector/reservoir populations and proximity) and to build a diagnostic engine to generate sophisticated alerts and web-based thematic map products for farmers and animal health professionals to have better situational awareness of animal disease and the risks for animal disease. Diagnostic clinical decision support systems will be built that can construct differential diseases to consider lists that will help to narrow the possibilities of what pathogen might be involve in a cluster of adverse animal health events. Because of the intense computational needs of processing many data streams in a timely fashion, the application will be built for a super-computing platform such as the 16-Teraflop IBM super-computer which was recently installed at the University of Kentucky.

Advantages for Doing the Work as a Multi-State Effort

There is a strong need to not only demonstrate the benefits of near-real-time cluster detection of animal health events at the local level but also to show that such a system can operate effectively in a multi-state environment. Ultimately, this system could provide a model for national and possibly international monitoring of animal health events for the purpose of early disease recognition.

Likely Impacts from Successfully Completing the Work

1. Further demonstration of the economic value of near-real-time disease monitoring in animals.
2. Further research the collection and analysis of additional data streams which relate to the risk of disease in animals and validation of the results.
3. Demonstrate that near-real-time monitoring of animal health events can be conducted in a multi-state environment for the benefit of all states involved.
4. Demonstrate how this system could become a sentinel for a bioterrorist attack or aid in early detection of emerging animal diseases.
5. Demonstrate how a system that provides early detection of animal diseases could be useful in identifying possible health risks to humans (e.g. zoonotic diseases, toxins).
6. Project might lead to becoming a national/international model for early detection of animal diseases.
7. Development of hand-held devices for bidirectional communication of diagnostic assistance tools for farmers, epidemiologists and veterinarians.

Types of Activities

Objectives

1. Build multi-state functionality into the existing disease cluster system
   
2. Expand on the data streams analyzed daily to aid in identifying risk factors for animal disease to include the collection of farm-level animal health events
   
3. Develop a sophisticated alerting mechanism for disease/event clusters that will alert appropriate emergency management and department of agricultural officials in the face of a foreign animal disease outbreak or an agroterrorist attack
   
4. Develop decision support algorithms that will aid in linking health events to specific diseases (i.e. build differential diagnosis lists) and suggest diagnostic tests that will confirm a diagnosis;
   
5. Port the existing health event analytical engine to the UK supercomputer to allow for expanded daily analysis of data streams.
   

Expected Outputs, Outcomes and/or Impacts

Projected Participation

View Appendix E: Participation

Literature Cited

Attachments

Land Grant Participating States/Institutions

KY, TN

Non Land Grant Participating States/Institutions