Modern biosurveillance relies on multiple sources of both pre-diagnostic and diagnostic data, up-dated daily, to discover disease outbreaks. Intrinsic to this effort are two assumptions: (1) the data being analyzed contain early indicators of a disease outbreak and (2) the outbreaks to be detected are not known a priori. However, in addition to outbreak indicators, syndromic data streams include such factors as day-of-week effects, seasonal effects, autocorrelation, and global trends. These explainable factors obscure unexplained outbreak events and their presence in the data violates standard control chart assumptions. Monitoring tools such as Shewhart, CuSum, and EWMA control charts will alert largely based on these explainable factors instead of on outbreaks. The goal of this paper is twofold: First, to describe a set of tools for identifying explainable patterns such as temporal dependence, and second, to survey and examine several data preconditioning methods that significantly reduce these explainable factors, yielding data better suited for monitoring using the popular control charts.

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.

In 2007-2008, the authors surveyed public health officials in 59 state, territorial, and selected large local jurisdictions in the United States regarding their conduct and use of syndromic surveillance. Fifty-two (88%) responded, representing areas comprising 94% of the United States population. Forty-three (83%) of the respondents reported conducting syndromic surveillance for a median of 3 years (range = 2 months to 13 years). Emergency department visits were the most common data source, used by 84%, followed by outpatient clinic visits (49%), over-the-counter medication sales (44%), calls to poison control centers (37%), and school absenteeism (35%). Among those who provided data on staffing and contract costs, the median number of staff dedicated to alert assessment was 1.0 (range 0.05 to 4), to technical system maintenance 0.6 (range zero to 3); and, among the two-thirds who reported using external contracts to support system maintenance, median annual contract costs were $95,000 (range = %5,500 to $1 million). Respondents rated syndromic surveillance as most useful for seasonal influenza monitoring, of intermediate usefulness for jurisdiction-wide trend monitoring and ad hoc analyses, and least useful for detecting typical community outbreaks. Nearly all plan to include syndromic surveillance as part of their surveillance strategy in the event of an influenza pandemic. Two thirds are either "highly" or "somewhat" likely to expand their use of syndromic surveillance within the next 2 years. Respondents from three state health departments who reported they did not conduct syndromic surveillance noted that local health departments in their states independently conducted syndromic surveillance. Syndromic surveillance is used widely throughout the United States. Although detection of outbreaks initially motivated investments in syndromic surveillance, other applications, notably influenza surveillance, are emerging as the main utility.