Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data

Abstract

BackgroundIn an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues’ two-component model, to two multivariate animal health datasets from Switzerland.ResultsIn our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities.ConclusionsStochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Autoregressive Models Applied to Time-Series Data in Veterinary Science

“…These studies were focused mostly on livestock (26). The temporal unit of data collection was most commonly day (17) or month (16), and the median period (years) covered by the datasets analyzed was 10 (IQR [5][6][7][8][9][10][11][12][13][14][15][16].…”

“…These methods can be considered a foundation in autoregressive methods for time-series analysis. Other methods—such as aberration detection algorithms, stochastic modeling approaches and machine-learning methods—can then be investigated for applications requiring long-term prediction ( 5 – 7 ).…”

An integer‐valued time series model for multivariate surveillance

“…The lower bound of the prediction interval is set equal to 0 since we are only interested in detecting positive deviations from the in‐control model. Each series flags an alarm at time t +1 if the corresponding observation lies outside the prediction interval, that is, if Finally, for the overall alarm, a majority rule can be defined, that is, flagging an alarm if a certain percentage of the series signals an alarm at the same point in time …”

Multichart Schemes for Detecting Changes in Disease Incidence

“…Statistical methods or algorithms have been widely applied to solve biosurveillance problems. These statistical methods or algorithms for monitoring bioterrorism, incidence, or outbreak of diseases can be categorized into temporal (see, for example, Reis [3] and Brookmeyer and Stroup [4]), spatio (Waller and Gotway [5] and Lawson and Kleinman [6]), spatio-temporal (Diggle [7] and Fricker [8]), multivariate temporal (see Vial [9]), multivariate spatial monitoring (see, for example, Corberán-Vallet [10]), multivariate spatio-temporal (Quick et al [11]), and Bayesian (Tzala [12]). Multivariate monitoring methods are extension of the univariate methods.…”