Diagnosis of cattle diseases endemic to sub-Saharan Africa : evaluating a low cost decision support tool in use by veterinary personnel

Background: Diagnosis is key to control and prevention of livestock diseases. In areas of sub-Saharan Africa where private practitioners rarely replace Government veterinary services reduced in effectiveness by structural adjustment programmes, those who remain lack resources for diagnosis and might benefit from decision support. Methodology/Principal Findings: We evaluated whether a low-cost diagnostic decision support tool would lead to changes in clinical diagnostic practice by fifteen veterinary and animal health officers undertaking primary animal healthcare in Uganda. The eight diseases covered by the tool included 98% of all bovine diagnoses made before or after its introduction. It may therefore inform proportional morbidity in the area; breed, age and geographic location effects were consistent with current epidemiological understanding. Trypanosomosis, theileriosis, anaplasmosis, and parasitic gastroenteritis were the most common conditions among 713 bovine clinical cases diagnosed prior to introduction of the tool. Thereafter, in 747 bovine clinical cases estimated proportional morbidity of fasciolosis doubled, while theileriosis and parasitic gastroenteritis were diagnosed less commonly and the average number of clinical signs increased from 3.5 to 4.9 per case, with 28% of cases reporting six or more signs compared to 3% beforehand. Anaemia/pallor, weakness and staring coat contributed most to this increase, approximately doubling in number and were recorded in over half of all cases. Finally, although lack of a gold standard hindered objective assessment of whether the tool improved the reliability of diagnosis, informative concordance and misclassification matrices yielded useful insights into its role in the diagnostic process. Conclusions/Significance: The diagnostic decision support tool covered the majority of diagnoses made before or after its introduction, leading to a significant increase in the number of clinical signs recorded, suggesting this as a key beneficial consequence of its use. It may also inform approximate proportional morbidity and represent a useful epidemiological tool in poorly resourced areas

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

Mate limitation in sea lice infesting wild salmon hosts : the influence of parasite sex ratio and aggregation

Mate limitation in dioecious parasite species has the potential to impact parasite population growth. Our focus of interest was the influence of parasite sex distribution among hosts on parasite reproduction and transmission dynamics for populations of ectoparasitic sea lice (Lepeophtheirus salmonis Krøyer) establishing on wild juvenile salmon hosts. The data included more than 139,000 out-migrating juvenile pink salmon (Oncorhynchus gorbuscha (Walbaum)) and chum salmon (Oncorhynchus keta (Walbaum)) in British Columbia, Canada, sampled over nine years. For almost all years, the sex ratio of the reproductive stages of the sea lice was female-biased. The probability of a female being able to mate (i.e., of being attached to a fish also carrying a male louse) increased with increasing parasite abundance and parasite aggregation. We compared, with expected modeling predictions, the observed prevalence of pairs of sea lice (i.e., one reproductive louse of each sex) on a given fish and the observed probability of a female being able to mate. These comparisons showed that male and female sea lice tend to be distributed together rather than separately on hosts. Distribution together means that sea lice are distributed randomly on hosts according to a common negative binomial distribution, whereas distribution separately means that males are distributed according to a negative binomial and females are distributed in their own negative binomial among hosts. Despite the tendency for distribution together we found that, in every year, at least 30% of reproductive female sea lice experience mate limitation. This Allee effect will result in submaximal rates of parasite reproduction at low parasite abundances and may limit parasite transmission. The work has important implications for salmon parasite management and the health both of captive farm salmon populations and migratory wild stocks. More broadly, these results demonstrate the potential impact of mate limitation as a constraint to the establishment and spread of wild ectoparasite populations

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

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

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

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

Sea lice monitoring on Atlantic salmon farms in New Brunswick, Canada : comparing audit and farm staff counts

Sea lice audits were performed by the Atlantic Veterinary College on commercial aquaculture sites in New Brunswick, Canada, in 2011. Although the primary objective was to verify that farms were reporting similar lice counts to third-party counts, more detailed comparisons were made to identify when lice counts were more likely to differ between the audit team and farm employees. A total of 28 sea lice audits were conducted on 16 sites between June and December 2011. During each audit, 10 cages were evaluated per site where possible, with ten fish per cage being evaluated by an audit technician and a further ten by a farm employee. Data analysis included descriptive statistics of lice counts by stage and limits of agreement plots. A random effects negative binomial model that accounted for clustering of cages within sites was applied to assess the effect of counter type and season on lice counts by stage. The results indicate that farms counts were generally in agreement with audit counts. However, when the average counts for chalimus and preadult (male and female) and adult male lice stages were high, farm counters were more likely to report a lower value. Higher lice counts were observed during autumn compared to summer especially for the adult female stage. Finally, there was a significant clustering effect for site and cage, with most of the variation attributable to site

Evaluation of water salinity effects on the sea lice Lepeophtheirus salmonis found on farmed Atlantic salmon in Muchalat Inlet, British Columbia, Canada

The sea louse Lepeophtheirus salmonis is a major ectoparasite of both farmed and wild salmonids that causes substantial economic losses to the salmon industry worldwide. However, in British Columbia (BC) sea lice do not typically represent a significant health threat to farmed salmon. Sea lice patterns on Atlantic salmon farms in BC are not fully understood, but it is believed they are highly influenced by sea water salinity levels, which vary dramatically over the year. The objective of this investigation was to evaluate the effects of changes in water salinity on mobile L. salmonis found in farmed salmonids in the Muchalat Inlet, BC, while controlling for potential confounding factors. Using daily farm-based salinity measurements over a 13-year period, we built different salinity metrics to summarize salinity drops within specific periods of time prior to sea lice sampling events. Our results suggest that reduced salinity negatively impacted mobile sea lice in three different ways: first, a direct effect on mobile lice, lasting no more than one day; second, an effect mediated by detrimental impacts on pre-mobile lice stages; and third, an effect possibly associated with reduced fecundity of parents of that lice cohort. These findings confirm the important role of salinity on sea lice population dynamics in BC, and contribute new knowledge which is useful in understanding sea lice patterns and determinants in this region. Relevance statement We provided evidence that salinity can naturally control sea lice in British Columbia