title: Retrospective time series analysis of veterinary laboratory data: preparing a historical baseline for cluster detection in syndromic surveillance.
abstract: The practice of disease surveillance has shifted in the last two decades towards the introduction of systems capable of early detection of disease. Modern biosurveillance systems explore different sources of pre-diagnostic data, such as patient's chief complaint upon emergency visit or laboratory test orders. These sources of data can provide more rapid detection than traditional surveillance based on case confirmation, but are less specific, and therefore their use poses challenges related to the presence of background noise and unlabelled temporal aberrations in historical data. The overall goal of this study was to carry out retrospective analysis using three years of laboratory test submissions to the Animal Health Laboratory in the province of Ontario, Canada, in order to prepare the data for use in syndromic surveillance. Daily cases were grouped into syndromes and counts for each syndrome were monitored on a daily basis when medians were higher than one case per day, and weekly otherwise. Poisson regression accounting for day-of-week and month was able to capture the day-of-week effect with minimal influence from temporal aberrations. Applying Poisson regression in an iterative manner, that removed data points above the predicted 95th percentile of daily counts, allowed for the removal of these aberrations in the absence of labelled outbreaks, while maintaining the day-of-week effect that was present in the original data. This resulted in the construction of time series that represent the baseline patterns over the past three years, free of temporal aberrations. The final method was thus able to remove temporal aberrations while keeping the original explainable effects in the data, did not need a training period free of aberrations, had minimal adjustment to the aberrations present in the raw data, and did not require labelled outbreaks. Moreover, it was readily applicable to the weekly data by substituting Poisson regression with moving 95th percentiles.
authors.affiliation: Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada. fdorea@upei.ca
authors.forename: Fernanda C
authors.initials: FC
authors.lastname: Dórea
authors.forename: Crawford W
authors.initials: CW
authors.lastname: Revie
authors.forename: Beverly J
authors.initials: BJ
authors.lastname: McEwen
authors.forename: W Bruce
authors.initials: WB
authors.lastname: McNab
authors.forename: David
authors.initials: D
authors.lastname: Kelton
authors.forename: Javier
authors.initials: J
authors.lastname: Sanchez
mesh_terms: D000818:Animals; D002417:Cattle; D002418:Cattle Diseases / Q000175:diagnosis* / Q000453:epidemiology; D015233:Models, Statistical; D009864:Ontario / Q000453:epidemiology; D016012:Poisson Distribution; D011159:Population Surveillance; D012189:Retrospective Studies