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

Monitoring livestock pregnancy loss

Systematically tracking and analysing reproductive loss in livestock helps with efforts to safeguard the health and productivity of food animals by identifying causes and high-risk areas.

Data-fed, needs-driven: Designing analytical workflows fit for disease surveillance

Syndromic surveillance has been an important driver for the incorporation of "big data analytics" into animal disease surveillance systems over the past decade. As the range of data sources to which automated data digitalization can be applied continues to grow, we discuss how to move beyond questions around the means to handle volume, variety and velocity, so as to ensure that the information generated is fit for disease surveillance purposes. We make the case that the value of data-driven surveillance depends on a "needs-driven" design approach to data digitalization and information delivery and highlight some of the current challenges and research frontiers in syndromic surveillance.

Can North American animal poison control center call data provide early warning of outbreaks associated with contaminated pet food? Using the 2007 melamine pet food contamination incident as a case study

The 2007 melamine pet food contamination incident highlighted the need for enhanced reporting of toxicological exposures and development of a national quantitative disease surveillance system for companion animals. Data from poison control centers, such as the Animal Poison Control Center (APCC), may be useful for conducting real-time surveillance in this population. In this study, we explored the suitability of APCC call data for early warning of toxicological incidents in companion animal populations by using a-priori knowledge of the melamine-related nephrotoxicosis outbreak. Patient and household-level information regarding possible toxicological exposures in dogs and cats reported to the APCC from 2005 to 2007, inclusive, were extracted from the APCC's AnTox database. These data were used to examine the impact of surveillance outcome, statistical methodology, analysis level, and call source on the ability to detect the outbreak prior to the voluntary recall issued by the pet food manufacturer. Retrospective Poisson temporal scan tests were applied for each combination of outcome, method, level, and call source. The results showed that month-adjusted scans using syndromic data may have been able to help detect the outbreak up to two months prior to the voluntary recall although the success of these methods varied across call sources. We also demonstrated covariate month-adjustment can lead to vastly different results based on the surveillance outcome and call source to which it is applied. This illustrates care should be taken prior to arbitrarily selecting a surveillance outcome and statistical model for surveillance efforts and warns against ignoring the impacts of call source or key covariates when applying quantitative surveillance methods to APCC call data since these factors can lead to very different results. This study provides further evidence that APCC call data may be useful for conducting surveillance in the US companion animal population and further exploratory analyses and validation studies are warranted.

Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs

A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the ‘gastroenteric’ MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived ‘gastroenteric’ MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives.

High real-time reporting of domestic and wild animal diseases following rollout of mobile phone reporting system in Kenya

Background

To improve early detection of emerging infectious diseases in sub-Saharan Africa (SSA), many of them zoonotic, numerous electronic animal disease-reporting systems have been piloted but not implemented because of cost, lack of user friendliness, and data insecurity. In Kenya, we developed and rolled out an open-source mobile phone-based domestic and wild animal disease reporting system and collected data over two years to investigate its robustness and ability to track disease trends.

Methods

The Kenya Animal Biosurveillance System (KABS) application was built on the Java® platform, freely downloadable for android compatible mobile phones, and supported by web-based account management, form editing and data monitoring. The application was integrated into the surveillance systems of Kenya’s domestic and wild animal sectors by adopting their existing data collection tools, and targeting disease syndromes prioritized by national, regional and international animal and human health agencies. Smartphone-owning government and private domestic and wild animal health officers were recruited and trained on the application, and reports received and analyzed by Kenya Directorate of Veterinary Services. The KABS application performed automatic basic analyses (frequencies, spatial distribution), which were immediately relayed to reporting officers as feedback.

Results

Of 697 trained domestic animal officers, 662 (95%) downloaded the application, and >72% of them started reporting using the application within three months. Introduction of the application resulted in 2- to 14-fold increase in number of disease reports when compared to the previous year (relative risk = 14, CI 13.8–14.2, p<0.001), and reports were more widely distributed. Among domestic animals, food animals (cattle, sheep, goats, camels, and chicken) accounted for >90% of the reports, with respiratory, gastrointestinal and skin diseases constituting >85% of the reports. Herbivore wildlife (zebra, buffalo, elephant, giraffe, antelopes) accounted for >60% of the wildlife disease reports, followed by carnivores (lions, cheetah, hyenas, jackals, and wild dogs). Deaths, traumatic injuries, and skin diseases were most reported in wildlife.

Conclusions

This open-source system was user friendly and secure, ideal for rolling out in other countries in SSA to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.

Enhancing the value of meat inspection records for broiler health and welfare surveillance: longitudinal detection of relational patterns

Background

Abattoir data are under-used for surveillance. Nationwide surveillance could benefit from using data on meat inspection findings, but several limitations need to be overcome. At the producer level, interpretation of meat inspection findings is a notable opportunity for surveillance with relevance to animal health and welfare. In this study, we propose that discovery and monitoring of relational patterns between condemnation conditions co-present in broiler batches at meat inspection can provide valuable information for surveillance of farmed animal health and welfare.

Results

Great Britain (GB)-based integrator meat inspection records for 14,045 broiler batches slaughtered in nine, four monthly intervals were assessed for the presence of surveillance indicators relevant to broiler health and welfare. K-means and correlation-based hierarchical clustering, and association rules analyses were performed to identify relational patterns in the data. Incidence of condemnation showed seasonal and temporal variation, which was detected by association rules analysis. Syndrome-related and non-specific relational patterns were detected in some months of meat inspection records. A potentially syndromic cluster was identified in May 2016 consisting of infection-related conditions: pericarditis, perihepatitis, peritonitis, and abnormal colour. Non-specific trends were identified in some months as an unusual combination of condemnation reasons in broiler batches.

Conclusions

We conclude that the detection of relational patterns in meat inspection records could provide producer-level surveillance indicators with relevance to broiler chicken health and welfare.

Factors associated with baseline mortality in Norwegian Atlantic salmon farming

In 2019, it was estimated that more than 50 million captive Atlantic salmon in Norway died in the final stage of their production in marine cages. This mortality represents a significant economic loss for producers and a need to improve welfare for farmed salmon. Single adverse events, such as algal blooms or infectious disease outbreaks, can explain mass mortality in salmon cages. However, little is known about the production, health, or environmental factors that contribute to their baseline mortality during the sea phase. Here we conducted a retrospective study including 1627 Atlantic salmon cohorts put to sea in 2014-2019. We found that sea lice treatments were associated with Atlantic salmon mortality. In particular, the trend towards non-medicinal sea lice treatments, including thermal delousing, increases Atlantic salmon mortality in the same month the treatment is applied. There were differences in mortality among production zones. Stocking month and weight were other important factors, with the lowest mortality in smaller salmon stocked in August-October. Sea surface temperature and salinity also influenced Atlantic salmon mortality. Knowledge of what affects baseline mortality in Norwegian aquaculture can be used as part of syndromic surveillance and to inform salmon producers on farming practices that can reduce mortality.

Exploiting Scanning Surveillance Data to Inform Future Strategies for the Control of Endemic Diseases: The Example of Sheep Scab

Scanning surveillance facilitates the monitoring of many endemic diseases of livestock in Great Britain, including sheep scab, an ectoparasitic disease of major welfare and economic burden. There is, however, a drive to improve the cost-effectiveness of animal health surveillance, for example by thoroughly exploiting existing data sources. By analysing the Veterinary Investigation Diagnosis Analysis (VIDA) database, this study aimed to enhance the use of existing scanning surveillance data for sheep scab to identify current trends, highlighting geographical "hotspots" for targeted disease control measures, and identifying a denominator to aid the interpretation of the diagnostic count data. Furthermore, this study collated and assessed the impact of past targeted disease control initiatives using a temporal aberration detection algorithm, the Farrington algorithm, to provide an evidence base towards developing cost-effective disease control strategies. A total of 2,401 positive skin scrapes were recorded from 2003 to 2018. A statistically significant decline in the number of positive skin scrapes diagnosed (p < 0.001) occurred across the study period, and significant clustering was observed in Wales, with a maximum of 47 positive scrapes in Ceredigion in 2007. Scheduled ectoparasite tests was also identified as a potential denominator for the interpretation of positive scrapes by stakeholders. Across the study period, 11 national disease control initiatives occurred: four in Wales, three in England, and four in Scotland. The majority (n = 8) offered free diagnostic testing while the remainder involved knowledge transfer either combined with free testing or skills training and the introduction of the Sheep Scab (Scotland) Order 2010. The Farrington algorithm raised 20 alarms of which 11 occurred within a period of free testing in Wales and one following the introduction of the Sheep Scab (Scotland) Order 2010. In summary, our analysis of the VIDA database has greatly enhanced our knowledge of sheep scab in Great Britain, firstly by identifying areas for targeted action and secondly by offering a framework to measure the impact of future disease control initiatives. Importantly this framework could be applied to inform future strategies for the control of other endemic diseases.

Day-1 Competencies for Veterinarians Specific to Health Informatics

In 2015, the American Association of Veterinary Medical Colleges (AAVMC) developed the Competency-Based Veterinary Education (CBVE) framework to prepare practice-ready veterinarians through competency-based education, which is an outcomes-based approach to equipping students with the skills, knowledge, attitudes, values, and abilities to do their jobs. With increasing use of health informatics (HI: the use of information technology to deliver healthcare) by veterinarians, competencies in HI need to be developed. To reach consensus on a HI competency framework in this study, the Competency Framework Development (CFD) process was conducted using an online adaptation of Developing-A-Curriculum, an established methodology in veterinary medicine for reaching consensus among experts. The objectives of this study were to (1) create an HI competency framework for new veterinarians; (2) group the competency statements into common themes; (3) map the HI competency statements to the AAVMC competencies as illustrative sub-competencies; (4) provide insight into specific technologies that are currently relevant to new veterinary graduates; and (5) measure panelist satisfaction with the CFD process. The primary emphasis of the final HI competency framework was that veterinarians must be able to assess, select, and implement technology to optimize the client-patient experience, delivery of healthcare, and work-life balance for the veterinary team. Veterinarians must also continue their own education regarding technology by engaging relevant experts and opinion leaders.

Potential and Challenges of Community-Based Surveillance in Animal Health: A Pilot Study Among Equine Owners in Switzerland

Animal owners' potential to observe and report clinical signs, as the persons with the closest contact to their animals, is an often neglected source of information in surveillance. Allowing community members other than health care professionals, such as animal owners, to report health events can contribute to close current surveillance gaps and enhance early detection. In the present study, we tested a community-based surveillance (CBS) approach in the equine community in Switzerland. We aimed at revealing the attitudes and intentions of equine owners toward reporting clinical signs by making use of an online questionnaire. We further set up and operated an online CBS tool, named Equi-Commun. Finally, we investigated potential reasons for the lack of its use by applying qualitative telephone interviews. The majority of the respondents of the online questionnaire (65.5%, 707/1,078) answered that they could see themselves reporting clinical observations of their equine. The multivariate logistic regression analysis indicated that French-speaking equine owners and those belonging to the positive attitude cluster are more likely to report to a CBS tool. Equi-Commun operated between October 2018 and December 2019 yet received only four reports. With the addition of qualitative interviews, we identified three critical, interlinked issues that may have led to the non-use of Equi-Commun within the Swiss equine community: (1) for successfully implementing CBS, the need for surveillance within the community of interest must be given; (2) the respective population under surveillance, here the equine, needs to show enough clinical cases for owners to be able to maintain the memory of an existing tool and its possible use; and (3) targeted and high effort communication of the system is key for its success. While CBS relying only on lay animal owners, complementary to existing surveillance systems, could potentially provide a good proxy of timely surveillance data, it is questionable whether the added value of generated surveillance knowledge is in balance with efforts necessary to implement a successful system. With this study, we showcased both the potential and challenges of CBS in animal health, as this may be of relevance and guidance for future initiatives.

Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making

The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.

Towards an integrated animal health surveillance system in Tanzania: making better use of existing and potential data sources for early warning surveillance

BACKGROUND

Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making. In Tanzania, the animal health surveillance system has been relying on a few data sources, which suffer from delays in reporting, underreporting, and high cost of data collection and transmission. The integration of data from multiple sources can enhance early detection and response to animal diseases and facilitate the early control of outbreaks. This study aimed to identify and assess existing and potential data sources for the animal health surveillance system in Tanzania and how they can be better used for early warning surveillance. The study used a mixed-method design to identify and assess data sources. Data were collected through document reviews, internet search, cross-sectional survey, key informant interviews, site visits, and non-participant observation. The assessment was done using pre-defined criteria.

RESULTS

A total of 13 data sources were identified and assessed. Most surveillance data came from livestock farmers, slaughter facilities, and livestock markets; while animal dip sites were the least used sources. Commercial farms and veterinary shops, electronic surveillance tools like AfyaData and Event Mobile Application (EMA-i) and information systems such as the Tanzania National Livestock Identification and Traceability System (TANLITS) and Agricultural Routine Data System (ARDS) show potential to generate relevant data for the national animal health surveillance system. The common variables found across most sources were: the name of the place (12/13), animal type/species (12/13), syndromes (10/13) and number of affected animals (8/13). The majority of the sources had good surveillance data contents and were accessible with medium to maximum spatial coverage. However, there was significant variation in terms of data frequency, accuracy and cost. There were limited integration and coordination of data flow from the identified sources with minimum to non-existing automated data entry and transmission.

CONCLUSION

The study demonstrated how the available data sources have great potential for early warning surveillance in Tanzania. Both existing and potential data sources had complementary strengths and weaknesses; a multi-source surveillance system would be best placed to harness these different strengths.

Early warning of infectious disease outbreaks on cattle-transport networks

Surveillance of infectious diseases in livestock is traditionally carried out at the farms, which are the typical units of epidemiological investigations and interventions. In Central and Western Europe, high-quality, long-term time series of animal transports have become available and this opens the possibility to new approaches like sentinel surveillance. By comparing a sentinel surveillance scheme based on markets to one based on farms, the primary aim of this paper is to identify the smallest set of sentinel holdings that would reliably and timely detect emergent disease outbreaks in Swiss cattle. Using a data-driven approach, we simulate the spread of infectious diseases according to the reported or available daily cattle transport data in Switzerland over a four year period. Investigating the efficiency of surveillance at either market or farm level, we find that the most efficient early warning surveillance system [the smallest set of sentinels that timely and reliably detect outbreaks (small outbreaks at detection, short detection delays)] would be based on the former, rather than the latter. We show that a detection probability of 86% can be achieved by monitoring all 137 markets in the network. Additional 250 farm sentinels—selected according to their risk—need to be placed under surveillance so that the probability of first hitting one of these farm sentinels is at least as high as the probability of first hitting a market. Combining all markets and 1000 farms with highest risk of infection, these two levels together will lead to a detection probability of 99%. We conclude that the design of animal surveillance systems greatly benefits from the use of the existing abundant and detailed animal transport data especially in the case of highly dynamic cattle transport networks. Sentinel surveillance approaches can be tailored to complement existing farm risk-based and syndromic surveillance approaches.

Real-Time Standard Analysis of Disease Investigation (SADI)-A Toolbox Approach to Inform Disease Outbreak Response

An incursion of an important exotic transboundary animal disease requires a prompt and intensive response. The routine analysis of up-to-date data, as near to real time as possible, is essential for the objective assessment of the patterns of disease spread or effectiveness of control measures and the formulation of alternative control strategies. In this paper, we describe the Standard Analysis of Disease Investigation (SADI), a toolbox for informing disease outbreak response, which was developed as part of New Zealand's biosecurity preparedness. SADI was generically designed on a web-based software platform, Integrated Real-time Information System (IRIS). We demonstrated the use of SADI for a hypothetical foot-and-mouth disease (FMD) outbreak scenario in New Zealand. The data standards were set within SADI, accommodating a single relational database that integrated the national livestock population data, outbreak data, and tracing data. We collected a well-researched, standardised set of 16 epidemiologically relevant analyses for informing the FMD outbreak response, including farm response timelines, interactive outbreak/network maps, stratified epidemic curves, estimated dissemination rates, estimated reproduction numbers, and areal attack rates. The analyses were programmed within SADI to automate the process to generate the reports at a regular interval (daily) using the most up-to-date data. Having SADI prepared in advance and the process streamlined for data collection, analysis and reporting would free a wider group of epidemiologists during an actual disease outbreak from solving data inconsistency among response teams, daily “number crunching,” or providing largely retrospective analyses. Instead, the focus could be directed into enhancing data collection strategies, improving data quality, understanding the limitations of the data available, interpreting the set of analyses, and communicating their meaning with response teams, decision makers and public in the context of the epidemic.

Autoregressive Models Applied to Time-Series Data in Veterinary Science

A time-series is any set of N time-ordered observations of a process. In veterinary epidemiology, our focus is generally on disease occurrence (the “process”) over time, but animal production, welfare or other traits might also be of interest. A common source of time-series datasets are animal disease monitoring and surveillance systems. Here, we scan the application of methods to analyse time-series data in the peer-reviewed, published literature. Based on this literature scan we focus on autocorrelation and illustrate the recommended steps using ARIMA (Autoregressive Integrated Moving Average Models) methods via analysis of a time-series of canine parvovirus (CPV) events in a pet dog population in Australia, 2009 to 2015. We conclude by identifying the barriers to the application of ARIMA methods in veterinary epidemiology and suggest some possible solutions. In the literature scan the selected 37 studies focused mostly on infectious and parasitic diseases, predominantly for analytical, rather than descriptive or predictive, purposes. Trends and seasonality were investigated, and autocorrelation analyzed, in most studies, most commonly using R software. An approach to analyzing autocorrelation using ARIMA methods was then illustrated using a time-series (week and month units) of CPV events in a pet dog population in Australia, reported to a national companion animal disease surveillance system. This time-series was derived by summing veterinarian reports of confirmed CPV diagnoses. We present data analysis output generated via the R statistical environment, and make this code available for the reader to apply to this or other time-series datasets. We also illustrate prediction of CPV events by rainfall as a covariate. Time-series analysis using ARIMA methods to understand and explore autocorrelation appears to be relatively uncommon in veterinary epidemiology. Some of the reasons might include limited availability of data of sufficient time unit length, lack of familiarity with analytical methods and available software, and how to best use the information generated. We recommend that wherever feasible, such time-series data be made available both for analysis and for methods development.

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits

Background

The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions.

Results

The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages.

Conclusions

The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.

Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year's weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015-2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14-25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14-25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

Spatio-temporal variations in mortality during the seawater production phase of Atlantic salmon (Salmo salar) in Norway

In a sustainable production of animals, monitoring and minimizing mortality must be a top priority. Systematic measuring of mortality over time can be used to evaluate the impact of changes in management and production strategies in Norway. To aid understanding of the potential for reducing mortality, we have used data from 2014 to 2018 to investigate the spatio-temporal patterns of mortality, by descriptive analyses and statistical modelling of possible determinants of mortality. The results show large differences in mortality across different production zones and between years. The areas with the highest density of farmed salmon are also the ones with highest mortality. The total cumulated mortality of farmed salmon increased from 32.3 million in 2014 to 35.2 million in 2018, corresponding to 14.3% and 15.8% of the standing stock. An initial higher mortality was observed during the first 3 months after stocking (mean: 1.5% [0.9%-8.6%] mortality/month). This was followed by a period of stability and lower mortality (mean: 0.8% [0.9%-3.1%] mortality/month), until month 10, when mortality started to increase again. The month of first stocking, the year of slaughter, production zone and number of months at sea were all found to be statistically significant determinants for mortality, with p-values < 1e-15.

Near Real-Time Monitoring of Clinical Events Detected in Swine Herds in Northeastern Spain

Novel techniques of data mining and time series analyses allow the development of new methods to analyze information relating to the health status of the swine population in near real-time. A swine health monitoring system based on the reporting of clinical events detected at farm level has been in operation in Northeastern Spain since 2012. This initiative was supported by swine stakeholders and veterinary practitioners of the Catalonia, Aragon, and Navarra regions. The system aims to evidence the occurrence of endemic diseases in near real-time by gathering data from practitioners that visited swine farms in these regions. Practitioners volunteered to report data on clinical events detected during their visits using a web application. The system allowed collection, transfer and storage of data on different clinical signs, analysis, and modeling of the diverse clinical events detected, and provision of reproducible reports with updated results. The information enables the industry to quantify the occurrence of endemic diseases on swine farms, better recognize their spatiotemporal distribution, determine factors that influence their presence and take more efficient prevention and control measures at region, county, and farm level. This study assesses the functionality of this monitoring tool by evaluating the target population coverage, the spatiotemporal patterns of clinical signs and presumptive diagnoses reported by practitioners over more than 6 years, and describes the information provided by this system in near real-time. Between January 2012 and March 2018, the system achieved a coverage of 33 of the 62 existing counties in the three study regions. Twenty-five percent of the target swine population farms reported one or more clinical events to the system. During the study period 10,654 clinical events comprising 14,971 clinical signs from 1,693 farms were reported. The most frequent clinical signs detected in these farms were respiratory, followed by digestive, neurological, locomotor, reproductive, and dermatological signs. Respiratory disorders were mainly associated with microorganisms of the porcine respiratory disease complex. Digestive signs were mainly related to colibacilosis and clostridiosis, neurological signs to Glässer's disease and streptococcosis, reproductive signs to PRRS, locomotor to streptococcosis and Glässer's disease, and dermatological signs to exudative epidermitis.

Machine learning for syndromic surveillance using veterinary necropsy reports

The use of natural language data for animal population surveillance represents a valuable opportunity to gather information about potential disease outbreaks, emerging zoonotic diseases, or bioterrorism threats. In this study, we evaluate machine learning methods for conducting syndromic surveillance using free-text veterinary necropsy reports. We train a system to detect if a necropsy report from the Wisconsin Veterinary Diagnostic Laboratory contains evidence of gastrointestinal, respiratory, or urinary pathology. We evaluate the performance of several machine learning algorithms including deep learning with a long short-term memory network. Although no single algorithm was superior, random forest using feature vectors of TF-IDF statistics ranked among the top-performing models with F1 scores of 0.923 (gastrointestinal), 0.960 (respiratory), and 0.888 (urinary). This model was applied to over 33,000 necropsy reports and was used to describe temporal and spatial features of diseases within a 14-year period, exposing epidemiological trends and detecting a potential focus of gastrointestinal disease from a single submitting producer in the fall of 2016.

Designing a Syndromic Bovine Mortality Surveillance System: Lessons Learned From the 1-Year Test of the French OMAR Alert Tool

Between May 2018 and 2019, a syndromic bovine mortality surveillance system (OMAR) was tested in 10 volunteer French départements (French intermediate-level administrative unit) to assess its performance in real conditions, as well as the human and financial resources needed to ensure normal functioning. The system is based on the automated weekly analysis of the number of cattle deaths reported by renderers in the Fallen Stock Data Interchange Database established in January 2011. In our system, every Thursday, the number of deaths is grouped by ISO week and small surveillance areas and then analyzed using traditional time-series analysis steps (cleaning, prediction, signal detection). For each of the five detection algorithms implemented (i.e., the exponentially weighted moving average chart, cumulative sum chart, Shewhart chart, Holt-Winters, and historical limits algorithms), seven detection limits are applied, giving a signal score from 1 (low excess mortality) to 7 (high excess mortality). The severity of excess mortality (alarm) is then classified into four categories, from very low to very high, by combining the signal scores, the relative excess mortality, and the persistence of the signal(s) over the previous 4 weeks. Detailed and interactive weekly reports and a short online questionnaire help pilot départements and the OMAR central coordination cell assess the performance of the system. During the 1-year test, the system showed highly variable sensitivity among départements. This variability was partly due not only to the demographic distribution of cattle (very few signals in low-density areas) but also to the renderer's delay in reporting to the Fallen Stock Data Interchange Database (on average, only 40% of the number of real deaths had been transmitted within week, with huge variations among départements). As a result, in the pilot départements, very few alarms required on-farm investigation and excess mortality often involved a small number of farms already known to have health or welfare problems. Despite its perfectibility, the system nevertheless proved useful in the daily work of animal health professionals for collective and individual surveillance. The test is still ongoing for a second year in nine départements to evaluate the effectiveness of the improvements agreed upon at the final meeting.

Improving the Utility of Voluntary Ovine Fallen Stock Collection and Laboratory Diagnostic Submission Data for Animal Health Surveillance Purposes: A Development Cycle

There are calls from policy-makers and industry to use existing data sources to contribute to livestock surveillance systems, especially for syndromic surveillance. However, the practical implications of attempting to use such data sources are challenging; development often requires incremental steps in an iterative cycle. In this study the utility of business operational data from a voluntary fallen stock collection service was investigated, to determine if they could be used as a proxy for the mortality experienced by the British sheep population. Retrospectively, Scottish ovine fallen stock collection data (2011-2014) were transformed into meaningful units for analysis, temporal and spatial patterns were described, time-series methods and a temporal aberration detection algorithm applied. Distinct annual and spatial trends plus seasonal patterns were observed in the three age groups investigated. The algorithm produced an alarm at the point of an historic known departure from normal (April 2013) for two age groups, across Scotland as a whole and in specific postcode areas. The analysis was then extended. Initially, to determine if similar methods could be applied to ovine fallen stock collections from England and Wales for the same time period. Additionally, Scottish contemporaneous laboratory diagnostic submission data were analyzed to see if they could provide further insight for interpretation of statistical alarms. Collaboration was required between the primary data holders, those with industry sector knowledge, plus veterinary, epidemiological and statistical expertise, in order to turn data and analytical outcomes into potentially useful information. A number of limitations were identified and recommendations were made as to how some could be addressed in order to facilitate use of these data as surveillance "intelligence." e.g., improvements to data collection and provision. A recent update of the fallen stock collections data has enabled a longer temporal period to be analyzed, with evidence of changes made in line with the recommendations. Further development will be required before a functional system can be implemented. However, there is potential for use of these data as: a proxy measure for mortality in the sheep population; complementary components in a future surveillance system, and to inform the design of additional surveillance system components.

Simulation Based Evaluation of Time Series for Syndromic Surveillance of Cattle in Switzerland

Choosing the syndrome time series to monitor in a syndromic surveillance system is not a straight forward process. Defining which syndromes to monitor in order to maximize detection performance has been recently identified as one of the research priorities in Syndromic surveillance. Estimating the minimum size of an epidemic that could potentially be detected in a specific syndrome could be used as a criteria for comparing the performance of different syndrome time series, and could provide some guidance for syndrome selection. The aim of our study was to estimate the potential value of different time series for building a national syndromic surveillance system for cattle in Switzerland. Simulations were used to produce outbreaks of different size and shape and to estimate the ability of each time series and aberration detection algorithm to detect them with high sensitivity, specificity and timeliness. Two temporal aberration detection algorithms were also compared: Holt-Winters generalized exponential smoothing (HW) and Exponential Weighted Moving Average (EWMA). Our results indicated that a specific aberration detection algorithm should be used for each time series. In addition, time series with high counts per unit of time had good overall detection performance, but poor detection performance for small epidemics making them of limited use for an early detection system. Estimating the minimum size of simulated epidemics that could potentially be detected in syndrome TS-event detection pairs can help surveillance system designers choosing the most appropriate syndrome TS to include in their early epidemic surveillance system.

Macroepidemiological aspects of porcine reproductive and respiratory syndrome virus detection by major United States veterinary diagnostic laboratories over time, age group, and specimen

This project investigates the macroepidemiological aspects of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by veterinary diagnostic laboratories (VDLs) for the period 2007 through 2018. Standardized submission data and PRRSV real-time reverse-transcriptase polymerase chain reaction (RT-qPCR) test results from porcine samples were retrieved from four VDLs representing 95% of all swine samples tested in NAHLN laboratories in the US. Anonymized data were retrieved and organized at the case level using SAS (SAS® Version 9.4, SAS® Institute, Inc., Cary, NC) with the use of PROC DATA, PROC MERGE, and PROC SQL scripts. The final aggregated and anonymized dataset comprised of 547,873 unique cases was uploaded to Power Business Intelligence-Power BI® (Microsoft Corporation, Redmond, Washington) to construct dynamic charts. The number of cases tested for PRRSV doubled from 2010 to 2018, with that increase mainly driven by samples typically used for monitoring purposes rather than diagnosis of disease. Apparent seasonal trends for the frequency of PRRSV detection were consistently observed with a higher percentage of positive cases occurring during fall or winter months and lower during summer months, perhaps due to increased testing associated with well-known seasonal occurrence of swine respiratory disease. PRRSV type 2, also known as North American genotype, accounted for 94.76% of all positive cases and was distributed across the US. PRRSV type 1, also known as European genotype, was geographically restricted and accounted for 2.15% of all positive cases. Co-detection of both strains accounted for 3.09% of the positive cases. Both oral fluid and processing fluid samples, had a rapid increase in the number of submissions soon after they were described in 2008 and 2017, respectively, suggesting rapid adoption of these specimens by the US swine industry for PRRSV monitoring in swine populations. As part of this project, a bio-informatics tool defined as Swine Disease Reporting System (SDRS) was developed. This tool has real-time capability to inform the US swine industry on the macroepidemiological aspects of PRRSV detection, and is easily adaptable for other analytes relevant to the swine industry.

Identification the Presence of Clade 2.3.2.1c-Avian Influenza H5N1, a Highly Pathogenic Virus in Iraq, 2018

For the first time in Iraq, we identified in March, 2018 the presence of a highly virulent avian influenza virus (AIV), H5N1 (Clade 2.3.2.1c), causing highly pathogenic avian influenza (HPAI) in poultry farms, Iraq,. The identification of the virus was done using a rapid serological test, a real time-qPCR, and glycoprotein gene sequencing. Using sequencing and phylogenetic analyses, the clade 2.3.2.1c virus was recorded to be clustered, with high similarity to Asian and West African AIV, HPAI H5N1 from Ivory Coast identified in 2015. According to our knowledge, there was no previous detection of the clade 2.3.2.1c made in Iraq. Our results provide evidence that high risk of HPAI H5 outbreaks might be present in Iraq, and this needs to lead to high quality surveillance targeting of wild and domestic birds for early diagnosis of HPAI. The current work provides feasible and accurate approaches for understanding the evolution of HPAI H5 virus in different countries around the world.

Complex System Approaches for Animal Health Surveillance

Many new and highly variable data are currently being produced by the many participants in farmed animal productions systems. These data hold the promise of new information with potential value for animal health surveillance. The current analytical paradigm for dealing with these new data is to implement syndromic surveillance systems, which focus mainly on univariate event detection methods applied to individual time series, with the goal of identifying epidemics in the population. This approach is relatively limited in the scope and not well-suited for extracting much of the additional information that is contained within these data. These approaches have value and should not be abandoned. However, an additional, new analytical paradigm will be needed if surveillance and disease control agencies wish to extract additional information from these data. We propose a more holistic analytical approach borrowed from complex system science that considers animal disease to be a product of the complex interactions between the many individuals, organizations and other factors that are involved in, or influence food production systems. We will discuss the characteristics of farmed animal food production systems that make them complex adaptive systems and propose practical applications of methods borrowed from complex system science to help animal health surveillance practitioners extract additional information from these new data.

Drivers for the development of an Animal Health Surveillance Ontology (AHSO)

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

Using longitudinal syndromic surveillance to describe small ruminant health in village production systems in Myanmar

A novel syndromic surveillance approach was used to describe small ruminant health in Myanmar, to help overcome limitations in disease diagnosis common in many parts of the world, especially in low and middle income countries (LMICs). Observations were made from July 2015 to June 2016 of ten clinical syndromes in 12 goat herds and sheep flocks owned by smallholders in the Central Dry Zone. Strengths and weaknesses to using syndromic surveillance in a village setting were identified using a formal surveillance evaluation framework, 'SERVAL'. Larger reporting teams made disproportionately more reports than smaller ones (86% compared to 14% of all reports, with a reporting rate ratio of 4.3 95% CI 3.5-5.4), which may have affected surveillance sensitivity. The benefits of the syndromic surveillance included its relatively low cost and ability to produce quantitative disease estimates that could be used to prioritise further disease investigation and extension activities. In particular, significant mortality was observed, with monthly mortality of 3.0% (95% CI 2.5-3.7%) and 0.28% (0.15-0.53%) in young and adult animals, respectively, and a population attributable fraction of mortality for young animals of 82% (68-91%). Mortality was associated with ill-thrift in young animals but had not previously been considered an important production-limiting condition in Myanmar. This information contributes to an understanding of the prevalence of excessive mortality in smallholder goat and sheep production systems. It is a practical example of the use of syndromic surveillance in a LMIC livestock production system, the results of which can direct future disease research, treatment and prevention to improve the health and productivity of small ruminants in Myanmar.

The value of necropsy reports for animal health surveillance

BACKGROUND

Animal health data recorded in free text, such as in necropsy reports, can have valuable information for national surveillance systems. However, these data are rarely utilized because the text format requires labor-intensive classification of records before they can be analyzed with using statistical or other software. In a previous study, we designed a text-mining tool to extract data from text in necropsy reports. In the current study, we used the tool to extract data from the reports from pig and cattle necropsies performed between 2000 and 2011 at the Institute of Animal Pathology (ITPA), University of Bern, Switzerland. We evaluated data quality in terms of credibility, completeness and representativeness of the Swiss pig and cattle populations.

RESULTS

Data was easily extracted from necropsy reports. Data quality in terms of completeness and validity varied a lot depending on the type of data reported. Diseases of the gastrointestinal system were reported most frequently (54.6% of pig submissions and 40.8% of cattle submissions). Diseases affecting serous membranes were reported in 16.0% of necropsied pigs and 27.6% of cattle. Respiratory diseases were reported in 18.3% of pigs and 21.6% of cattle submissions.

CONCLUSIONS

This study suggests that extracting data from necropsy reports can provide information of value for animal health surveillance. This data has potential value for monitoring endemic disease syndromes in different age and production groups, or for early detection of emerging or re-emerging diseases. The study identified data entry and other errors that could be corrected to improve the quality and validity of the data. Submissions to veterinary diagnostic laboratories have selection biases and these should be considered when designing surveillance systems that include necropsy reports.

Translating Big Data into Smart Data for Veterinary Epidemiology

The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing “big” data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having “big data” to create “smart data,” with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.