Integrating novel data streams to support biosurveillance in commercial livestock production systems in developed countries: challenges and opportunities

Quality of Data Recording and Antimicrobial Use in a Municipal Veterinary Clinic in Ghana

The recording of antimicrobial use data is critical for the development of interventions for the containment of antimicrobial resistance. This cross-sectional study assessed whether dissemination activities and recommendations made after an operational research (OR) study in 2021 resulted in better data recording and improved the use of antimicrobials in a rural veterinary clinic. Routinely collected data from treatment record books were compared between 2013 and 2019 (pre-OR) and from July 2021 to April 2023 (post-OR). The most common animals presenting for care in the the pre - and post OR periods were dogs (369 and 206, respectively). Overall, antimicrobial use in animals increased from 53% to 77% between the two periods. Tetracycline was the most commonly used antimicrobial (99%) during the pre-OR period, while Penicillin-Streptomycin was the most commonly used antimicrobial (65%) during the post-OR period. All animals that received care at the clinic were documented in the register during both periods. Whereas the diagnosis was documented in 269 (90%) animals in the post-OR period compared to 242 (47%) in the pre-OR period, the routes and dosages were not adequately recorded during the both periods. Therefore, the quality of data recording was still deficient despite the dissemination and the recommendations made to some key stakeholders. Recommendations are made for a standardized antimicrobial reporting tool, refresher training, and continuous supervisory visits to the clinic.

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.

Evaluations of the Disease Surveillance Centre network in Scotland: What parts has it reached?

Regular evaluation is a prerequisite for systems that provide surveillance of animal populations. Scotland's Rural College Veterinary vices' Disease Surveillance Centre (DSC) network plays an integral part in surveillance to detect new and re-emerging threats within animal populations, predominantly livestock. In ronse to surveillance reviews and proposed changes to the network, an initial evaluation of diagnostic submissions data in 2010 to mid-2012 established a baseline "footprint", while highlighting challenges with the data. In this recenaluation for the period 2013-2018, we developed a new denominator using a combination of agricultural census and movement data, to identify relevant holdings more accurately. Iterative discussions between those processing submissions data ahose involved in collection at source took place to understand the intricacies of the data, establish the most appropriate dataset, and develop the processes required to optimise the data extraction and cleansing. The subsequent descriptive analysis identifies the number of diatic submissions, the number of unique holdings making submissions to the network and shows that both the surrounding geographic region of, and maximum dise to the closest DSC vary greatly between centres. Analysis of those submissions classed as farm animal post-mortems also highlights the effect of distance to the closest DSC. Whether specific differences between the time periods are due to changes in the behavior of the submitting holdior the data extraction and cleaning processes was difficult to disentangle. However, with the improved techniques producing better data to work with, a new baseline foot prior the network has been created. This provides information that can help policy makers and surveillance providers make decisions about service provision and evaluate the impact of future changes. Additionally, thtputs of these analyses can provide feedback to those employed in the service, providing evidence of what they are achieving and why changes to data collection processes and ways of working are being made. In a different setting, er data will be available and different challenges may arise. However, the fundamental principles highlighted in these evaluations and the solutions developed should be of interest to any surveillance providers generating similar diagnostic data.

Factors influencing the performance of voluntary farmer disease reporting in passive surveillance systems: A scoping review

The impacts of exotic disease incursions on livestock industries can be mitigated by having robust surveillance systems in place that decrease the time between disease introduction and detection. An important component of this is having farmers routinely observe their animals for indications of clinical disease, recognise the existence of problems, and then decide to notify their veterinarian or animal health authorities. However, as highlighted by this literature review, farmers are believed to be underreporting clinical events due to factors such as (1) uncertainty around the clinical signs and situations that warrant reporting, (2) fear over the social and economic consequences from both positive and false positive reports, (3) negative beliefs regarding the efficacy and outcomes of response measures, (4) mistrust and dissatisfaction with animal health authorities, (5) absence of sufficiently attractive financial and non-financial incentives for submitting reports, and (6) poor awareness of the procedures involved with the submission, processing, and response to reports. There have been few formal studies evaluating the efficacy of different approaches to increasing farmer engagement with disease reporting. However, there is a recognised need for any proposed solutions to account for farmer knowledge and experience with assessing their own farm situation as well as the different identities, motivations, and beliefs that farmers have about their role in animal health surveillance systems. Empowering farmers to take a more active role in developing these solutions is likely to become even more important as animal health authorities increasingly look to establish public-private partnerships for biosecurity governance.

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.

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.

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.

Emerging Applications of Machine Learning in Food Safety

Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data streams, including text, transactional, and trade data, have seen emerging applications enabled by a machine learning approach, such as prediction of antibiotic resistance, source attribution of pathogens, and foodborne outbreak detection and risk assessment. In this article, we provide a gentle introduction to machine learning in the context of food safety and an overview of recent developments and applications. With many of these applications still in their nascence, general and domain-specific pitfalls and challenges associated with machine learning have begun to be recognized and addressed, which are critical to prospective use and future deployment of large data sets and their associated machine learning models for food safety applications.

Using farmer observations for animal health syndromic surveillance: Participation and performance of an online enhanced passive surveillance system

The challenge of animal health surveillance is to provide the information necessary to appropriately inform disease prevention and control activities within the constraints of available resources. Syndromic surveillance of farmers' disease observations can improve animal health data capture from extensive livestock farming systems, especially where data are not otherwise being systematically collected or when data on confirmed aetiological diagnoses are unavailable at the disease level. As it is rarely feasible to recruit a truly random sample of farmers to provide observational reports, directing farmer sampling to align with the surveillance objectives is a reasonable and practical approach. As long as potential bias is recognised and managed, farmers who will report reliably can be desirable participants in a surveillance system. Thus, one early objective of a surveillance program should be to identify characteristics associated with reporting behaviour. Knowledge of the demographic and managerial characteristics of good reporters can inform efforts to recruit additional farms into the system or aid understanding of potential bias of system reports. We describe the operation of a farmer syndromic surveillance system in Victoria, Australia, over its first two years from 2014 to 2016. Survival analysis and classification and regression tree analysis were used to identify farm level factors associated with 'reliable' participation (low non-response rates in longitudinal reporting). Response rate and timeliness were not associated with whether farmers had disease to report, or with different months of the year. Farmers keeping only sheep were the most reliable and timely respondents. Farmers < 43 years of age had lower response rates than older farmers. Farmers with veterinary qualifications and those working full-time on-farm provided less timely reports than other educational backgrounds and farmers who worked part-time on-farm. These analyses provide a starting point to guide recruitment of participants for surveillance of farmers' observations using syndromic surveillance, and provide examples of strengths and weaknesses of syndromic surveillance systems for extensively-managed livestock. Once farm characteristics associated with reliable participation are known, they can be incorporated into surveillance system design in accordance with the objectives of the system.

Unweaving tangled mortality and antibiotic consumption data to detect disease outbreaks - Peaks, growths, and foresight in swine production

As our capacity to collect and store health data is increasing, a new challenge of transforming data into meaningful information for disease monitoring and surveillance has arisen. The aim of this study was to explore the potential of using livestock mortality and antibiotic consumption data as a proxy for detecting disease outbreaks at herd level. Changes in the monthly records of mortality and antibiotic consumption were monitored in Danish swine herds that became positive for porcine reproductive and respiratory syndrome (PRRS) and porcine pleuropneumonia. Laboratory serological results were used to identify herds that changed from a negative to a positive status for the diseases. A dynamic linear model with a linear growth component was used to model the data. Alarms about state changes were raised based on forecast errors, changes in the growth component, and the values of the retrospectively smoothed values of the growth component. In all cases, the alarms were defined based on credible intervals and assessed prior and after herds got a positive disease status. The number of herds with alarms based on mortality increased by 3% in the 3 months prior to laboratory confirmation of PRRS-positive herds (Se = 0.47). A 22% rise in the number of weaner herds with alarms based on the consumption of antibiotics for respiratory diseases was found 1 month prior to these herds becoming PRRS-positive (Se = 0.22). For porcine pleuropneumonia-positive herds, a 10% increase in antibiotic consumption for respiratory diseases in sow herds was seen 1 month prior to a positive result (Se = 0.5). Monitoring changes in mortality data and antibiotic consumption showed changes at herd level prior to and in the same month as confirmation from diagnostic tests. These results also show a potential value for using these data streams as part of surveillance strategies.

Assessing the use of diagnostic laboratory accession data to support national bovine viral diarrhoea control in New Zealand

Aims: To assess the suitability of using existing national diagnostic laboratory testing data to support national bovine viral diarrhoea (BVD) research, surveillance, and control in New Zealand. Methods: Data on laboratory accessions for BVD diagnostic testing in New Zealand from 1 January 2015 to 31 December 2017 were provided by four commercial veterinary diagnostic companies. The data were integrated into a single dataset containing the unique accession number, sample submission date, farm location (territorial authority level), test type (bulk milk antibody-ELISA, bulk milk PCR, serum antibody-ELISA, blood/serum/tissue antigen-ELISA, or blood/serum/tissue PCR), and test results. Estimates for the number of registered cattle farms in each territorial authority were generated from the National Animal Identification and Tracing database. Results were summarised for July 2015 to June 2016 and July 2016 to June 2017. Results: There was a total of 59,007 unique BVD diagnostic test accessions including 39,920 (67.6%) for bulk milk antibody-ELISA, 27,832 (47.2%) for bulk milk PCR, 3,229 (5.5%) for serum antibody-ELISA, 9,132 (15.5%) for blood/serum/tissue antigen-ELISA, and 7,122 (12.1%) for blood/serum/tissue PCR. Of the 17,946 accessions for blood/serum/tissue samples, 4,316 (24.0%) were missing the herd production type and 6,678 (37.2%) were missing the animals age. Approximately 7,000/10,958 (65%) dairy herds and 1,600/43,611 (4%) beef herds were conducting annual BVD screening tests. In 2016/2017, the prevalence of accessions with ≥1 BVD-positive result was 40.6% for bulk milk antibody, 6.4% for bulk milk PCR, 45.6% for serum antibody, and 9.8% for blood/serum/tissue antigen-ELISA or PCR tests. There was substantial regional variation in both the percentage of herds testing for BVD and the prevalence of positive accessions. Following pooled serum antibody-ELISA, only 175/604 (29.0%) beef herds and 177/566 (31.3%) dairy herds had recorded follow-up testing. Conclusions and Clinical Relevance: Laboratory diagnostic accession data has the potential to provide valuable insights about BVD epidemiology in New Zealand, but there are significant limitations in the data collected and discrepancies in the different systems that each laboratory uses to measure, interpret, and record diagnostic data. There is a strong need to develop a more consistent national system for recording and sharing BVD test results to support BVD management at farm and industry levels. Abbreviations: BVD: Bovine viral diarrhoea; Ct: Cycle threshold; NAIT: National Animal Identification and Tracing; NZVP: New Zealand Veterinary Pathology; PI: Persistently infected; S/P: Sample to positive control.

Syndromic surveillance by veterinary practitioners: a pilot study in the pig sector

Traditional indicator-based livestock surveillance has been focused on case definitions, definitive diagnoses and laboratory confirmation. The use of syndromic disease surveillance would increase the population base from which animal health data are captured and facilitate earlier detection of new and re-emerging threats to animal health. Veterinary practitioners could potentially play a vital role in such activities. In a pilot study, specialist private veterinary practitioners (PVP) working in the English pig industry were asked to collect and transfer background data and disease incident reports for pig farms visited during the study period. Baseline data from 110 pig farms were received, along with 68 disease incident reports. Reports took an average of approximately 25 minutes to complete. Feedback from the PVPs indicated that they saw value in syndromic surveillance. Maintenance of anonymity in the outputs would be essential, as would timely access for the PVPs to relevant information on syndromic trends. Further guidance and standardisation would also be required. Syndromic surveillance by PVPs is possible for the pig industry. It has potential to fill current gaps in the collection of animal health data, as long as the engagement and participation of data providers can be obtained and maintained.

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.

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.