Fernanda C. Dórea

Veterinary syndromic surveillance: Current initiatives and potential for development.

This paper reviews recent progress in the development of syndromic surveillance systems for veterinary medicine. Peer-reviewed and grey literature were searched in order to identify surveillance systems that explicitly address outbreak detection based on systematic monitoring of animal population data, in any phase of implementation. The review found that developments in veterinary syndromic surveillance are focused not only on animal health, but also on the use of animals as sentinels for public health, representing a further step towards One Medicine. The main sources of information are clinical data from practitioners and laboratory data, but a number of other sources are being explored. Due to limitations inherent in the way data on animal health is collected, the development of veterinary syndromic surveillance initially focused on animal health data collection strategies, analyzing historical data for their potential to support systematic monitoring, or solving problems of data classification and integration. Systems based on passive notification or data transfers are now dealing with sustainability issues. Given the ongoing barriers in availability of data, diagnostic laboratories appear to provide the most readily available data sources for syndromic surveillance in animal health. As the bottlenecks around data source availability are overcome, the next challenge is consolidating data standards for data classification, promoting the integration of different animal health surveillance systems, and also the integration to public health surveillance. Moreover, the outputs of systems for systematic monitoring of animal health data must be directly connected to real-time decision support systems which are increasingly being used for disease management and control.

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt–Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.

Background Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes–syndromic surveillance–using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. Methods This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. Results High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F1-micro = 0.994 and F1-macro = .955), however the classification process is not transparent to the domain experts. Conclusion The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input.

Syndromic Surveillance Using Veterinary Laboratory Data: Algorithm Combination and Customization of Alerts

Background Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals. Methods This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed. Results The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described. Conclusion The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.

Retrospective time series analysis of veterinary laboratory data: Preparing a historical baseline for cluster detection in syndromic surveillance

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 patients 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.

Exploring the Surveillance Potential of Mortality Data: Nine Years of Bovine Fallen Stock Data Collected in Catalonia (Spain)

The potential of fallen stock data to monitor the health status of animal populations has been noted in previous studies. However, further research is required to implement these systems for surveillance. This work presents a novel approach to determining the baselines associated with bovine fallen stock, comparing patterns between subpopulations and identifying subpopulations in which an abnormal event may occur. This study was based on data from 193,873 disposal visits carried out between 2004 and 2012 across a total of 2,991 bovine farms. Proxy measurements such as the number of collections carried out and the weight of carcasses collected were used. Both outcomes were aggregated weekly at different geographical scales for three production types (beef cattle, dairy cattle and heifer fattening). The analysis of these data combined autoregressive integrated moving average modelling and hierarchical time series methods.The three production types exhibited historical baselines that differed notably from one another. Based on the 757 beef cattle farms monitored, the mean number of collections registered per week at the regional level was 37 (range: 10–83). This series was relatively constant over time and showed a marked yearly seasonality. In contrast, for the 426 dairy cattle farms the mean number of disposal visits registered weekly was 121 (range: 71–180), showing half-yearly and yearly seasonality and a marked increase over the period monitored. From the 1,808 heifer fattening farms the mean number of disposal visits was 248 (range: 166–357) and the pattern presented a marked alternating trend over time. These patterns were assessed and compared at regional, provincial, county and municipal levels. The use of hierarchical time series approaches appeared to be a useful tool for comparing the patterns within different subpopulations over time as well as for assessing the spatial extent to which various abnormal events could be detected.

Pilot simulation study using meat inspection data for syndromic surveillance: use of whole carcass condemnation of adult cattle to assess the performance of several algorithms for outbreak detection.

The objective of this study was to assess the performance of several algorithms for outbreak detection based on weekly proportions of whole carcass condemnations. Data from one French slaughterhouse over the 2005-2009 period were used (177 098 slaughtered cattle, 0.97% of whole carcass condemnations). The method involved three steps: (i) preparation of an outbreak-free historical baseline over 5 years, (ii) simulation of over 100 years of baseline time series with injection of artificial outbreak signals with several shapes, durations and magnitudes, and (iii) assessment of the performance (sensitivity, specificity, outbreak detection precocity) of several algorithms to detect these artificial outbreak signals. The algorithms tested included the Shewart p chart, confidence interval of the negative binomial model, the exponentially weighted moving average (EWMA); and cumulative sum (CUSUM). The highest sensitivity was obtained using a negative binomial algorithm and the highest specificity with CUSUM or EWMA. EWMA sensitivity was too low to select this algorithm for efficient outbreak detection. CUSUMs performance was complementary to the negative binomial algorithm. The use of both algorithms on real data for a prospective investigation of the whole carcass condemnation rate as a syndromic surveillance indicator could be relevant. Shewart could also be a good option considering its high sensitivity and simplicity of implementation.

Pasture is a risk factor for Toxoplasma gondii infection in fattening pigs

As consumer awareness of animal welfare increases throughout Europe, housing of pigs in more animal-friendly systems is becoming more common. There is concern that these free-range and organic management systems increase the prevalence of zoonotic meat-borne pathogens, such as Toxoplasma gondii. In this study we compared the seroprevalence of T. gondii between commercial fattening pigs raised on conventional and on organic farms in Sweden. Furthermore, potential associations between presence of T. gondii antibodies and type of production, access to pasture, and geographical region were analysed. A significant difference in T. gondii seroprevalence was found between conventional (1%) and organic pigs (8%). The higher odds of seropositivity in organic production was attributed to pasture access specifically (OR=1.8 for a one-month increase in length of pasture exposure). This study shows that the prevalence of T. gondii in Swedish conventional pigs is low. However, as pigs with access to pasture are at higher risk of infection and because the demand for animal-friendly production systems is increasing, there is an obvious need to practically manage the higher T. gondii presence in products from pigs raised in organic systems with outdoor access.

Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016)

This review presents the current initiatives and potential for development in the field of animal health surveillance (AHSyS), 5 years on from its advent to the front of the veterinary public health scene. A systematic review approach was used to document the ongoing AHSyS initiatives (active systems and those in pilot phase) and recent methodological developments. Clinical data from practitioners and laboratory data remain the main data sources for AHSyS. However, although not currently integrated into prospectively running initiatives, production data, mortality data, abattoir data, and new media sources (such as Internet searches) have been the objective of an increasing number of publications seeking to develop and validate new AHSyS indicators. Some limitations inherent to AHSyS such as reporting sustainability and the lack of classification standards continue to hinder the development of automated syndromic analysis and interpretation. In an era of ubiquitous electronic collection of animal health data, surveillance experts are increasingly interested in running multivariate systems (which concurrently monitor several data streams) as they are inferentially more accurate than univariate systems. Thus, Bayesian methodologies, which are much more apt to discover the interplay among multiple syndromic data sources, are foreseen to play a big part in the future of AHSyS. It has become clear that early detection of outbreaks may not be the principal expected benefit of AHSyS. As more systems will enter an active prospective phase, following the intensive development stage of the last 5 years, the study envisions AHSyS, in particular for livestock, to significantly contribute to future international-, national-, and local-level animal health intelligence, going beyond the detection and monitoring of disease events by contributing solid situation awareness of animal welfare and health at various stages along the food-producing chain, and an understanding of the risk management involving actors in this value chain.

Vector-borne disease surveillance in livestock populations: A critical review of literature recommendations and implemented surveillance (BTV-8) in five European countries

Preparedness against vector-borne threats depends on the existence of a long-term, sustainable surveillance of vector-borne disease and their relevant vectors. This work reviewed the availability of such surveillance systems in five European countries (Denmark, France, The Netherlands, Sweden and United Kingdom, part of the CoVetLab network). A qualitative assessment was then performed focusing on surveillance directed particularly to BTV-8. Information regarding surveillance activities were reviewed for the years 2008 and 2012. The results were then complemented with a critical scoping review of the literature aimed at identifying disease surveillance strategies and methods that are currently suggested as best suited to target vector-borne diseases in order to guide future development of surveillance in the countries in question. Passive surveillance was found to be efficient for early detection of diseases during the early phase of introduction into a free country. However, its value diminished once the disease has been established in a territory. Detection of emerging diseases was found to be very context and area specific, and thus active surveillance designs need to take the available epidemiological, ecological and entomological information into account. This was demonstrated by the effectiveness of the bulk milk surveillance in detecting the first case in Sweden, highlighting the need for output based standards to allow the most effective, context dependent, surveillance strategies to be used. Preparedness was of fundamental importance in determining the timeliness of detection and control in each country and that this in turn was heavily influenced by knowledge of emerging diseases in neighboring countries. Therefore it is crucial to share information on outbreaks between researchers and decision-makers and across borders continuously in order to react timely in case of an outbreak. Furthermore, timely reaction to an outbreak was heavily influenced by availability of control measures (vaccines), which is also strengthened if knowledge is shared quickly between countries. The assessment of the bluetongue surveillance in the affected countries showed that the degree of voluntary engagement varied, and that it is important to engage the public by general awareness and dissemination of results. The degree of engagement will also aid in establishing a passive surveillance system.