A scenario tree model for the Canadian Notifiable Avian Influenza Surveillance System and its application to estimation of probability of freedom and sample size determination

Assessing freedom from chronic wasting disease in semi-domesticated reindeer in Norway and Sweden

Establishing freedom from disease is a key component of surveillance and may have direct consequences for trade and economy. Transboundary populations pose challenges in terms of variable legislation, efforts, and data availability between countries, often limiting surveillance efficiency. Chronic wasting disease (CWD) is a contagious prion disease of cervids. The long incubation period and slow initial epidemic growth make it notoriously difficult to detect CWD in the early phase of an epidemic. The recent emergence of CWD in wild reindeer in Norway poses a threat to approximately 250,000 semi-domesticated reindeer in Norway and 250,000 in Sweden, including transboundary populations. Here, we provide a first analysis of surveillance data (2016-2022) from all reindeer districts in Norway and Sweden to determine the probability of freedom from CWD infection. During the six years, 6017 semi-domesticated reindeer were tested in Sweden and 51,974 in Norway. Most samples came from healthy slaughtered animals (low risk). Reindeer use large and remote areas and (high risk) samples from fallen stock and animals with clinical signs were difficult to obtain. A scenario tree model was run for seven different set of values for the input parameters (design prevalence within and between districts, probability of introduction, and relative risks) to determine the effect on surveillance sensitivity. At the national level, the mean probability of disease freedom was 59.0 % in Sweden and 87.0 % in Norway by 2021. The most marked effect on sensitivity was varying the design prevalence both within and between districts. Uncertainty about relative risk ratios affected sensitivity for Sweden more than for Norway, due to the higher proportion of animals in the high-risk group in the former (13.8 % vs. 2.1 %, respectively). A probability of disease freedom of 90 % or higher was reached in 8.2 % of the 49 districts in Sweden and 43.5 % of the 46 districts in Norway for a design prevalence of 0.5 %. The probability of freedom remained below 60 % in 29 districts (59.2 %) in Sweden and 10 districts (21.7 %) in Norway. At the national level, only Norway had a sufficiently large number of samples to reach a probability of more than 95 % of disease freedom within a period of 10 years. Our cross-border assessment forms an important knowledge base for designing future surveillance efforts depending on the spatial pattern of prevalence of CWD and risk of spread.

Assessment of the cost-effectiveness of alternative bovine tuberculosis surveillance protocols in French cattle farms using the mixed interferon gamma test

Periodic screening in farms, using intradermal cervical comparative tuberculin test (ICCT), is a component of the French ante mortem surveillance of bovine tuberculosis (bTB). Previous studies have estimated the cost-effectiveness of the French mandatory bTB screening protocols. In these protocols, a second ICCT (ICCT2) is performed 42 days after the first one (ICCT1), either on the entire herd (strict protocol) or in series on animals with non-negative results (reactors) to ICCT1 (compliant protocol). The 42-days interval reduced protocols' cost-effectiveness. To minimize this interval, we suggested two alternative protocols, in which a mixed interferon gamma test (IFNMIX), with better sensitivity than ICCT2 and comparable specificity, replaces the ICCT2, and is carried out directly after the ICCT1. In the strict alternative protocol, reactors to ICCT1 are culled to perform laboratory analyses (PCR, bacteriology). Negative results to these analyses imply the IFNMIX testing of the entire herd. In the compliant alternative protocol, only reactor(s) to ICCT1 are tested with IFNMIX, and animals with positive results to IFNMIX are culled for laboratory analyses. We evaluated these protocols through scenario tree modelling. The estimated cost-effectiveness indexes showed that the compliant alternative protocol was the most efficient. The strict protocols (mandatory and alternative) were never the most efficient, but were the most effective. Therefore, using IFNMIX instead of ICCT2 may be useful in reducing the costs of the compliant protocol used when the probability of infection is considered low. The strict alternative protocol may become more attractive would IFNMIX's price decreased.

Assessment of bovine tuberculosis surveillance effectiveness in French wildlife: An operational approach

Bovine tuberculosis (bTB) is a chronic, zoonotic, bacterial disease mostly caused by Mycobacterium bovis, which can affect both domestic and wild species. France was officially declared bTB-free in 2001 but faced since 2004 an increase of the prevalence in cattle. Since 2001, bTB has been detected in several wild species: red deer (Cervus elaphus), roe deer (Capreolus capreolus), wild boar (Sus scrofa) and badger (Meles meles). Infected wild species constitute a major threat, because they may contribute to the maintenance of the infection in cattle and prevent eradication. In 2011, a surveillance system, Sylvatub, was implemented nationwide, to monitor the epidemiological status of bTB in mainland France. Our objective in this study was to assess the effectiveness of one of Sylvatub's passive surveillance system components (SSCs), which is based on the visual inspection of hunted animals (wild boars, red and roe deer) throughout mainland France. The following effectiveness criteria were evaluated: individual-level and component group-level positive and negative predictive values, and individual-level and component group-level probabilities of type I error ("false-positive" error) and type II error ("false-negative" error). These criteria were estimated quantitatively, at the scale of the département (a French administrative area of similar size to a county), with a stochastic scenario tree model. Individual negative predictive values were high, whereas individual positive predictive values were poor, whatever the species considered, and the training of hunters did not improve these effectiveness criteria sufficiently. The individual-level probability of type I error was relatively low, but the individual-level probability of type II error was generally high and was therefore an issue. However, increasing the proportion of trained hunters decreased this probability effectively. At group level, the size of the population surveyed had a marked impact on the effectiveness criteria: both the component group-level negative predictive value and the component group-level probability of type II error decreased rapidly with increasing population size. The conclusions drawn from such rationale may directly benefit stakeholders and actors in the field in their day to day practice of the surveillance processes. Thus, the assessment method used in this article presents the advantage of being operational, as well as being applicable to any surveillance system.

Integrating data of veterinarians' practices in assessing the cost effectiveness of three components of the bovine tuberculosis surveillance system by intradermal tuberculin testing in French cattle farms through a scenario-tree approach

Disease surveillance systems' effectiveness relies on participants following prescribed practices. We developed a general method to improve a previous cost-effectiveness evaluation of three French screening program protocols for bovine tuberculosis (bTB) to account for the practices of participants by scenario tree modelling. This method relies on: 1) semi-directive interviews of participants to identify the variability of practices and potentially influential factors, and to understand the sociological context; 2) a quantitative survey, based on multiple-choice questions, to quantify various practices and identify significantly influential factors by multivariable regression analyses; 3) addition of the scenario-tree nodes corresponding to the practices and their influential factors and configuration of the new limbs according to the data of the quantitative survey. We used this approach to integrate data concerning veterinary practices and identify some failures to conform to regulatory guidelines regarding intradermal cervical comparative tuberculin test (SICCT) (testing and notification of non-negative results). Such nonconformities appeared to be mainly caused by cattle restraint issues and the perception of veterinarians of the bTB control program. Indeed, their perception of that program significantly influenced veterinarians' practices. We modelled the influence of the SICCT practices on the SICCT results. The incorporation of these data led to a major decrease of the herd sensitivity estimations relative to the previous assessments that did not incorporate data of practices (15% to 42% decrease). This result shows the important impact of veterinarians' practices and their influencing factors (such as perception of the bTB control program) on the effectiveness of the surveillance system.

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.

Routine clinical inspections in Norwegian marine salmonid sites: A key role in surveillance for freedom from pathogenic viral haemorrhagic septicaemia (VHS)

Since the mid-1980s, clinical inspections of aquaculture sites carried out on a regular basis by authorized veterinarians and fish health biologists (known as fish health services: FHS) have been an essential part of aquatic animal health surveillance in Norway. The aims of the present study were (1) to evaluate the performance of FHS routine clinical inspections for the detection of VHS and (2) to explore the effectiveness of risk-based prioritisation of FHS inspections for demonstrating freedom from VHS in marine salmonid sites in Norway. A stochastic simulation model was developed to estimate site sensitivity (SeS), population sensitivity (SeP), and probability of freedom (PFree). The estimation of SeS takes into consideration the probability that FHS submit samples if a site is infected, the probability that a sample is tested if submitted, the effective probability of infection in fish with clinical signs, laboratory test sensitivity, and the number of tested samples. SeP and PFree were estimated on a monthly basis over a 12 month period for six alternative surveillance scenarios and included the risk factors: region, species, area production density, and biosecurity level. Model results indicate that the current surveillance system, based on routine inspections by the FHS has a high capability for detecting VHS and that there is a high probability of freedom from VHS in Norwegian marine farmed salmonids (PFree >95%). Sensitivity analysis identified the probabilities that samples are submitted and submitted samples are tested, as the most influential input variables. The model provides a supporting tool for evaluation of potential changes in the surveillance strategy, and can be viewed as a platform for similar exotic viral infectious diseases in marine salmonid farming in Norway, if they share similar risk factors.

Modelling the species jump: towards assessing the risk of human infection from novel avian influenzas

The scientific understanding of the driving factors behind zoonotic and pandemic influenzas is hampered by complex interactions between viruses, animal hosts and humans. This complexity makes identifying influenza viruses of high zoonotic or pandemic risk, before they emerge from animal populations, extremely difficult and uncertain. As a first step towards assessing zoonotic risk of influenza, we demonstrate a risk assessment framework to assess the relative likelihood of influenza A viruses, circulating in animal populations, making the species jump into humans. The intention is that such a risk assessment framework could assist decision-makers to compare multiple influenza viruses for zoonotic potential and hence to develop appropriate strain-specific control measures. It also provides a first step towards showing proof of principle for an eventual pandemic risk model. We show that the spatial and temporal epidemiology is as important in assessing the risk of an influenza A species jump as understanding the innate molecular capability of the virus. We also demonstrate data deficiencies that need to be addressed in order to consistently combine both epidemiological and molecular virology data into a risk assessment framework.

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.

Capture-recapture approaches and the surveillance of livestock diseases: A review

In disease surveillance, capture-recapture approaches have been used to estimate the frequency of endemic diseases monitored by imperfect surveillance systems. A standard output of these techniques is an estimate of the sensitivity of the surveillance. In addition, capture-recapture applications contribute to a better understanding of the disease detection processes and of the relationships between different surveillance data sources, and help identify variables associated with the under-detection of diseases. Although capture-recapture approaches have long been used in public health, their application to livestock disease surveillance is only recent. In this paper, we review the different capture-recapture approaches applied in livestock disease surveillance, and discuss their benefits and limitations in the light of the characteristics of the surveillance and control practices used in animal health.

A rationale to unify measurements of effectiveness for animal health surveillance

Surveillance systems produce data which, once analysed and interpreted, support decisions regarding disease management. While several performance measures for surveillance are in use, no theoretical framework has been proposed yet with a rationale for defining and estimating effectiveness measures of surveillance systems in a generic way. An effective surveillance system is a system whose data collection, analysis and interpretation processes lead to decisions that are appropriate given the true disease status of the target population. Accordingly, we developed a framework accounting for sampling, testing and data interpretation processes, to depict in a probabilistic way the direction and magnitude of the discrepancy between "decisions that would be made if the true state of a population was known" and the "decisions that are actually made upon the analysis and interpretation of surveillance data". The proposed framework provides a theoretical basis for standardised quantitative evaluation of the effectiveness of surveillance systems. We illustrate such approaches using hypothetical surveillance systems aimed at monitoring the prevalence of an endemic disease and at detecting an emerging disease as early as possible and with an empirical case study on a passive surveillance system aiming at detecting cases of Highly Pathogenic Avian Influenza cases in Vietnamese poultry.

Contribution of Meat Inspection to the surveillance of poultry health and welfare in the European Union

In the European Union, Meat Inspection (MI) aims to protect public health by ensuring that minimal hazardous material enters in the food chain. It also contributes to the detection and monitoring of animal diseases and welfare problems but its utility for animal surveillance has been assessed partially for some diseases only. Using the example of poultry production, we propose a complete assessment of MI as a health surveillance system. MI allows a long-term syndromic surveillance of poultry health but its contribution is lowered by a lack of data standardization, analysis and reporting. In addition, the probability of case detection for 20 diseases and welfare conditions was quantified using a scenario tree modelling approach, with input data based on literature and expert opinion. The sensitivity of MI appeared to be very high to detect most of the conditions studied because MI is performed at batch level and applied to a high number of birds per batch.