Adapting a scenario tree model for freedom from disease as surveillance progresses: The Canadian notifiable avian influenza model

Enhancing Insights into Australia's Gonococcal Surveillance Programme through Stochastic Modelling

Continued surveillance of antimicrobial resistance is critical as a feedback mechanism for the generation of concerted public health action. A characteristic of importance in evaluating disease surveillance systems is representativeness. Scenario tree modelling offers an approach to quantify system representativeness. This paper utilises the modelling approach to assess the Australian Gonococcal Surveillance Programme's representativeness as a case study. The model was built by identifying the sequence of events necessary for surveillance output generation through expert consultation and literature review. A scenario tree model was developed encompassing 16 dichotomous branches representing individual system sub-components. Key classifications included biological sex, clinical symptom status, and location of healthcare service access. The expected sensitivities for gonococcal detection and antibiotic status ascertainment were 0.624 (95% CI; 0.524, 0.736) and 0.144 (95% CI; 0.106, 0.189), respectively. Detection capacity of the system was observed to be high overall. The stochastic modelling approach has highlighted the need to consider differential risk factors such as sex, health-seeking behaviours, and clinical behaviour in sample generation. Actionable points generated by this study include modification of clinician behaviour and supplementary systems to achieve a greater contextual understanding of the surveillance data generation process.

Development and evaluation of a new method to combine clinical impression survey data with existing laboratory data for veterinary syndromic surveillance with the Canada West Swine Health Intelligence Network (CWSHIN)

The Canada West Swine Health Intelligence Network (CWSHIN) is a surveillance system imbedded in an intelligence network. It has been conducting syndromic surveillance in the four western provinces of Canada since 2012. The quarterly activities include repeated clinical impression surveys, compilation of laboratory data, discussion of trends with an expert group (practitioners, laboratory diagnosticians) and swine health reports for producers and swine practitioners. However, due to the lack of standardized population identifiers across data sources usual methods of combining data could not be applied and the collated data were not being fully utilized and analysed. Therefore in 2019, CWSHIN underwent a substantial review resulting in the "Next Generation CWSHIN". The objectives of this study were to develop and evaluate a new data merging method to combine CWSHIN's clinical impression survey and laboratory data; and to provide examples of analyses and modeling based on these data. The data for analysis were restricted to repeated clinical impression surveys (2019-2020) from veterinary practitioners and laboratory diagnostic data (2016-2020). Merging surveillance data from existing sources can be challenging. Therefore, as an alternative to merge data using a hierarchy of population identifiers, we developed a Disease Map to link surveillance data from all our data-sources. The resulting Data Repository allowed monitoring of temporal trends of syndromes, clinical diseases, and laboratory identified organisms, but it cannot provide estimates of disease occurrence. Two main reasons were the lack of denominators and using existing data on routine diagnostic tests. Therefore, discussion in the expert group (veterinary practitioners, laboratory diagnosticians, swine health experts) was critical to the system's success. Based on repeated clinical impression surveys a stochastic scenario tree model for freedom from Foot and Mouth Disease (CWSHIN Blister model) was also developed. In conclusion, the method to link existing data systems from multiple divergent sources by means of a Disease Map improved CWSHIN's veterinary syndromic surveillance. Together the Data Repository and Disease map provided flexibility to monitor temporal trends, define populations and diseases, and allow analysis. However, it is critical that the surveillance is coupled with a good intelligence network that can help interpret the results and disseminate knowledge to veterinarians and producers.

A modified TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) applied to choosing appropriate selection methods in ongoing surveillance for Avian Influenza in Canada

To achieve an appropriate and efficient sample in a surveillance program, the goals of the program should drive a careful consideration of the selection method or combination of selection methods to be applied. Therefore, the ongoing analysis and assessment of a surveillance system may include an assessment of the ability of the applied selection methods to generate an appropriate sample. There may be opinions from many technical experts (TEs) and many criteria to consider in a surveillance system so there is a need for methods to combine knowledge, priorities and preferences from a group of TEs. This paper proposes a modified weighted and unweighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis to choose selection methods in surveillance. An example from the Canadian Notifiable Avian Influenza surveillance (CanNAISS) is used to illustrate the method as this surveillance offers unique data with multiple selection methods and subpopulations. The primary objective was to assess the performance of the different selection methods applied in CanNAISS, from 2008 to 2013, in three subpopulations (A-C). A modified TOPSIS (weighted and unweighted) analyses is proposed to aggregate preferences from three TEs and to identify the selection method that was closest to the ideal solution agreed upon by the TEs. Criteria weights were provided individually by three TEs. For the group decision making, internal and external aggregation approaches were used with arithmetic and geometric means. The results of the weighted modified TOPSIS analysis showed that the selection methods that used farm registries yielded high estimates of the relative closeness to ideal-solution. The ranking of selection methods based on the modified TOPSIS weighted analysis, conducted at the individual and group decision making levels were similar. Regardless of the aggregation approach used (internal or external) in group decision making, the use of the arithmetic and geometric means yielded similar estimates of relative closeness to ideal-solution. The unweighted modified TOPSIS analysis yielded similar estimates of the relative closeness to the ideal-solution and therefore making the interpretation of the results difficult. The weighted modified TOPSIS analysis contributed to an informed decision on the best selection method to apply in CanNAISS. The weighted modified TOPSIS analysis is a straightforward and suitable technique to address decision making problems where the profile of the ideal and non-ideal solutions is known a priori by the decision makers.

Sampling Methodology to Maximize the Efficient Use of National Abattoir Surveillance: Using Archived Sera to Substantiate Freedom From Bluetongue Virus Infection in Ireland

In recent years, there has been increasing recognition of the value of multiple data sources available to fulfill surveillance objectives, and the use of these has been applied to address many questions relating to animal health surveillance. In Ireland, we face a slightly different problem, namely, best use of an existing surveillance resource (serological samples collected over many years from cull cows at slaughter), which has been used to substantiate freedom from Brucella abortus following its successful eradication in 2009. In this study, we evaluate a sampling methodology to use this resource to substantiate freedom from bluetongue virus (BTV) infection. An examination of the degree to which cull cows were resident in the same herd throughout the midge biting season showed that, of 50,640 samples collected between 17 October and 23 December 2016, 80.2% were from animals resident in the same herd between 01 April 2016 and 2 months prior to their slaughter date, 74.1% for 1 month prior, 70.1% for 2 weeks prior, 66.4% for 1 week prior, and 56.4% up to 1 day prior to slaughter. An examination was made of the degree to which individual samples within the same 88-well frozen storage block came from geographically clustered herds, whether from a concentration of animals from the same herd in a single block, or from clustering around the slaughterhouse where the samples were taken. On the basis of these analyses, a sampling strategy was derived aimed at minimizing the number of storage blocks which needed to be thawed, whilst ensuring a large enough and representative sample, geographically stratified according to the bovine population of 51 squares, each 45 × 45 km, covering the entirety of Ireland. None of the 503 samples tested were positive for BTV, providing reassurance of national BTV freedom. More broadly, the study demonstrates the use of abattoir-based serological samples collected for one large scale surveillance programme in surveillance for other bovine infections.

Scenario tree model for animal disease freedom framed in the OIE context using the example of a generic swine model for Aujeszky's disease in commercial swine in Canada

"Freedom from animal disease" is an ambiguous concept that may have a different meaning in trade and science. For trade alone, there are different levels of freedom from OIE listed diseases. A country can: be recognized by OIE to be "officially free"; self-declare freedom, with no official recognition by the OIE; or report animal disease as absent (no occurrence) in six-monthly reports. In science, we apply scenario tree models to calculate the probability of a population being free from disease at a given prevalence to provide evidence of freedom from animal disease. Here, we link science with application by describing how a scenario tree model may contribute to a country's claim of freedom from animal disease. We combine the idea of a standardized presentation of scenario tree models for disease freedom and having a similar model for two different animal diseases to suggest that a simple generic model may help veterinary authorities to build and evaluate scenario tree models for disease freedom. Here, we aim to develop a generic scenario tree model for disease freedom that is: animal species specific, population specific, and has a simple structure. The specific objectives were: to explore the levels of freedom described in the OIE Terrestrial Animal Health Code; to describe how scenario tree models may contribute to a country's claim of freedom from animal disease; and to present a generic swine scenario tree model for disease freedom in Canada's domestic (commercial) swine applied to Aujeszky's disease (AD). In particular, to explore how historical survey data, and data mining may affect the probability of freedom and to explore different sampling strategies. Finally, to frame the generic scenario tree model in the context of Canada's claim of freedom from AD. We found that scenario tree models are useful to support a country's claim of freedom either as "recognized officially free" or as part of a self-declaration but the models should not stand alone in a claim. The generic AD scenario tree model demonstrated the benefit of combining three sources of surveillance data and helped to design the surveillance for the next year. The generic AD scenario model is one piece in Canada's self-declaration of freedom from AD. The model is strongly supported by the fact that AD has never been detected in Canada.

Comparing capture-recapture methods for estimation of the size of small and medium-sized populations using empirical data on commercial turkey farms in Canada

The study objectives were (1) to conduct a systematic review of the performance of capture-recapture methods; (2) to use empirical data to estimate population size in a small-sized population (turkey breeder farms) and a medium-sized population (meat turkey farms) by applying two-source capture-recapture methods (the Lincoln-Petersen, the Chapman, and Chao's lower-bound estimators) and multi-source capture-recapture methods (the log-linear modeling and sample coverage approaches); and (3) to compare the performance of these methods in predicting the true population sizes (2007 data). Our set-up was unique in that we knew the population sizes for turkey breeder farms (99) and meat turkey farms (592) in Canada in 2007, which we applied as our true population sizes, and had surveillance data from the Canadian Notifiable Avian Influenza Surveillance System (2008-2012). We defined each calendar year of sampling as a data source. We confirmed that the two-source capture-recapture methods were sensitive to the violation of the local independence assumption. The log-linear modeling and sample coverage approaches yielded estimates that were closer to the true population sizes than were the estimates provided by the two-source methods for both populations. The performance of both multi-source capture-recapture methods depended on the number of data sources analyzed and the size of the population. Simulation studies are recommended to better understand the limits of each multi-source capture-recapture method.