Evaluation of Microbiology Orders from a Veterinary Diagnostic Laboratory as a Potential Data Source for Early Outbreak Detection

Animals continue to be recognized as a potential source of surveillance data for detecting emerging infectious diseases, bioterrorism preparedness, pandemic influenza preparedness, and detection of other zoonotic diseases. Detection of disease outbreaks in animals remains mostly dependent upon systems that are disease specific and not very timely. Most zoonotic disease outbreaks are detected only after they have spread to humans. The use of syndromic surveillance methods (outbreak surveillance using pre-diagnostic data) in animals is a possible solution to these limitations. The authors examine microbiology orders from a veterinary diagnostics laboratory (VDL) as a possible data source for early outbreak detection. They establish the species representation in the data, quantify the potential gain in timeliness, and use a CuSum method to study counts of microorganisms, animal species, and specimen collection sites as potential early indicators of disease outbreaks. The results indicate that VDL microbiology orders might be a useful source of data for a surveillance system designed to detect outbreaks of disease in animals earlier than traditional reporting systems.