Bluetongue surveillance system in Belgium: A stochastic evaluation of its risk-based approach effectiveness

Scenario tree modelling to inform surveillance design for maintaining freedom from Coxiella burnetii infection in Australian commercial dairy goat herds

We used scenario tree methods to determine how different disease detection methods might be used to provide quantitative evidence that Australian dairy goat herds are free of coxiellosis. The aim of our proposed C. burnetii surveillance programme is to find evidence of the absence of antigen as well as evidence of an absence of an immune response to C. burnetii infection in individual dairy goat herds. We defined a C. burnetii infected dairy goat herd as a herd in which at least one doe was showing evidence of either active infection or past C. burnetii exposure using four candidate surveillance system components (SSCs): (1) testing of individual doe whole blood using the C. burnetii com1 PCR; (2) testing of individual doe whole blood using the C. burnetii ELISA; (3) testing bulk tank milk (BTM) using the com1 PCR and the C. burnetii ELISA; and (4) investigations of abortions and stillborn kids submitted to a diagnostic laboratory for testing. Of eight candidate surveillance strategies (combinations of the SSCs listed above) individual doe ELISAs every six months combined with monthly BTM PCR and ELISA testing returned the highest surveillance system sensitivity of 0.963 (95% probability interval [PI] 0.911-0.982) for the lowest cost, at AUD 28.94 (95% PI 28.38-30.59) over a 12-month period, for every one percent increase in surveillance system sensitivity. Assuming a probability of disease freedom of 0.10 at the start of the surveillance program and a probability of C. burnetii introduction per month of 0.01 we estimate that 95% confidence that C. burnetii was absent from a herd could be achieved after a single round of individual doe ELISAs followed by period of 6 consecutive monthly BTM PCR and ELISA tests. The results of this study show that selection of the most efficient combination of surveillance system components requires a good understanding of initial herd C. burnetii status and the probability of introduction of infection and how this may change over time. Scenario tree analyses results have provided insight into the key determinants of C. burnetii detection ability.

A framework for evaluating health system surveillance sensitivity to support public health decision-making for malaria elimination: a case study from Indonesia

Background

The effectiveness of a surveillance system to detect infections in the population is paramount when confirming elimination. Estimating the sensitivity of a surveillance system requires identifying key steps in the care-seeking cascade, from initial infection to confirmed diagnosis, and quantifying the probability of appropriate action at each stage. Using malaria as an example, a framework was developed to estimate the sensitivity of key components of the malaria surveillance cascade.

Methods

Parameters to quantify the sensitivity of the surveillance system were derived from monthly malaria case data over a period of 36 months and semi-quantitative surveys in 46 health facilities on Java Island, Indonesia. Parameters were informed by the collected empirical data and estimated by modelling the flow of an infected individual through the system using a Bayesian framework. A model-driven health system survey was designed to collect empirical data to inform parameter estimates in the surveillance cascade.

Results

Heterogeneity across health facilities was observed in the estimated probability of care-seeking (range = 0.01–0.21, mean ± sd = 0.09 ± 0.05) and testing for malaria (range = 0.00–1.00, mean ± sd = 0.16 ± 0.29). Care-seeking was higher at facilities regularly providing antimalarial drugs (Odds Ratio [OR] = 2.98, 95% Credible Intervals [CI]: 1.54–3.16). Predictably, the availability of functioning microscopy equipment was associated with increased odds of being tested for malaria (OR = 7.33, 95% CI = 20.61).

Conclusions

The methods for estimating facility-level malaria surveillance sensitivity presented here can help provide a benchmark for what constitutes a strong system. The proposed approach also enables programs to identify components of the health system that can be improved to strengthen surveillance and support public-health decision-making.

Improving laboratory diagnostic capacities of emerging diseases using knowledge mapping

Over the last decade, European countries faced several emerging and re-emerging animal diseases as well as zoonotic diseases. During these episodes, the laboratory diagnostic capabilities were a key factor to rapidly control and/or eradicate them. Because of the associated socio-economic and health consequences, it is crucial to react rapidly and efficiently, not only during crisis but also in peacetime (i.e. preparedness). However, to date, there is no published method to identify diseases with diagnostic gaps and to prioritize assays to be implemented. This study was conducted based on the outcome of a prioritization exercise in which 29 epizootic and exotic diseases with high risk of emergence or re-emergence in Belgium (Bianchini et al., [2020] Transboundary and Emerging Diseases, 67(1), 344-376) were listed. Knowledge mapping was used to visualize and identify gaps in the diagnostic procedures for different epidemiological scenarios at national level. To fill these gaps, an overview of diagnostic capabilities at national and international level (laboratories and kits providers or manufacturers) as well as the published assays in the scientific literature and the prescribed assays by international institutions and kits providers was carried out. The outcome of this study revealed the usefulness of knowledge mapping as a tool to identify gaps and ultimately gain insight on alternatives for better preparedness and responsiveness. While this exercise was limited to Belgium, we believe this exercise can benefit other countries and thereby enhancing knowledge sharing and collaboration to increase diagnostic capabilities for a common list of (re-) emerging diseases in crisis situation.

Effectiveness and cost-benefit study to encourage herd owners in a cost sharing vaccination programme against bluetongue serotype-8 in Belgium

Bluetongue (BT) is a ruminant viral infectious disease transmitted by Culicoides spp. midges. In 2006, when bluetongue virus serotype 8 (BTV-8) appeared for the first time in Northern Europe, it rapidly spread and infected a large proportion of animals. BThas a significant economic impact due to a direct effect on animal health and to an indirect effect in disrupting international trade of animals and animal products. In spring 2008, a compulsory subsidized vaccination programme in Europe resulted in a drastic decrease in the number of reported cases. However, due to the turn-over of the population, without a continuous vaccination programme, the animal population was becoming progressively susceptible. Vaccination would enable Belgium to maintain its status of freedom from infection of BTV-8 that could possibly be re-introduced. Subsidizing it could be an incentive to convince more farmers to vaccinate. To finance this programme, both decision-makers and stakeholders need to be persuaded by the effectiveness and the cost-benefit of vaccination. The study evaluated the effectiveness of vaccination against BTV-8 in Belgium. The change in serology which has shown the effectiveness of the vaccine to induce antibody production has been significantly associated with the time between the first injection and the sampling date and the number of injections of the primo-vaccination. This study also clearly confirms the benefit of vaccination by reducing economic impact of treatment and production losses, especially in dairy cattle. Based on a participating epidemiological approach, a national voluntary and subsidized vaccination was accepted, and permitted Belgium to vaccinate more than 9,000 herds in 1 month. Because this mass vaccination occurred before the vector season, it probably helped Belgium remain free from BTV-8.

Active animal health surveillance in European Union Member States: gaps and opportunities

Animal health surveillance enables the detection and control of animal diseases including zoonoses. Under the EU-FP7 project RISKSUR, a survey was conducted in 11 EU Member States and Switzerland to describe active surveillance components in 2011 managed by the public or private sector and identify gaps and opportunities. Information was collected about hazard, target population, geographical focus, legal obligation, management, surveillance design, risk-based sampling, and multi-hazard surveillance. Two countries were excluded due to incompleteness of data. Most of the 664 components targeted cattle (26·7%), pigs (17·5%) or poultry (16·0%). The most common surveillance objectives were demonstrating freedom from disease (43·8%) and case detection (26·8%). Over half of components applied risk-based sampling (57·1%), but mainly focused on a single population stratum (targeted risk-based) rather than differentiating between risk levels of different strata (stratified risk-based). About a third of components were multi-hazard (37·3%). Both risk-based sampling and multi-hazard surveillance were used more frequently in privately funded components. The study identified several gaps (e.g. lack of systematic documentation, inconsistent application of terminology) and opportunities (e.g. stratified risk-based sampling). The greater flexibility provided by the new EU Animal Health Law means that systematic evaluation of surveillance alternatives will be required to optimize cost-effectiveness.

Effectiveness and Cost Efficiency of Different Surveillance Components for Proving Freedom and Early Detection of Disease: Bluetongue Serotype 8 in Cattle as Case Study for Belgium, France and the Netherlands

Quick detection and recovery of country's freedom status remain a constant challenge in animal health surveillance. The efficacy and cost efficiency of different surveillance components in proving the absence of infection or (early) detection of bluetongue serotype 8 in cattle populations within different countries (the Netherlands, France, Belgium) using surveillance data from years 2006 and 2007 were investigated using an adapted scenario tree model approach. First, surveillance components (sentinel, yearly cross-sectional and passive clinical reporting) within each country were evaluated in terms of efficacy for substantiating freedom of infection. Yearly cross-sectional survey and passive clinical reporting performed well within each country with sensitivity of detection values ranging around 0.99. The sentinel component had a sensitivity of detection around 0.7. Secondly, how effective the components were for (early) detection of bluetongue serotype 8 and whether syndromic surveillance on reproductive performance, milk production and mortality data available from the Netherlands and Belgium could be of added value were evaluated. Epidemic curves were used to estimate the timeliness of detection. Sensitivity analysis revealed that expected within-herd prevalence and number of herds processed were the most influential parameters for proving freedom and early detection. Looking at the assumed direct costs, although total costs were low for sentinel and passive clinical surveillance components, passive clinical surveillance together with syndromic surveillance (based on reproductive performance data) turned out most cost-efficient for the detection of bluetongue serotype 8. To conclude, for emerging or re-emerging vectorborne disease that behaves such as bluetongue serotype 8, it is recommended to use passive clinical and syndromic surveillance as early detection systems for maximum cost efficiency and sensitivity. Once an infection is detected and eradicated, cross-sectional screening for substantiating freedom of infection and sentinel for monitoring the disease evolution are recommended.

Application of syndromic surveillance on routinely collected cattle reproduction and milk production data for the early detection of outbreaks of Bluetongue and Schmallenberg viruses

This study aimed to evaluate the use of routinely collected reproductive and milk production data for the early detection of emerging vector-borne diseases in cattle in the Netherlands and the Flanders region of Belgium (i.e., the northern part of Belgium). Prospective space-time cluster analyses on residuals from a model on milk production were carried out to detect clusters of reduced milk yield. A CUSUM algorithm was used to detect temporal aberrations in model residuals of reproductive performance models on two indicators of gestation length. The Bluetongue serotype-8 (BTV-8) epidemics of 2006 and 2007 and the Schmallenberg virus (SBV) epidemic of 2011 were used as case studies to evaluate the sensitivity and timeliness of these methods. The methods investigated in this study did not result in a more timely detection of BTV-8 and SBV in the Netherlands and BTV-8 in Belgium given the surveillance systems in place when these viruses emerged. This could be due to (i) the large geographical units used in the analyses (country, region and province level), and (ii) the high level of sensitivity of the surveillance systems in place when these viruses emerged. Nevertheless, it might be worthwhile to use a syndromic surveillance system based on non-specific animal health data in real-time alongside regular surveillance, to increase the sense of urgency and to provide valuable quantitative information for decision makers in the initial phase of an emerging disease outbreak.