Assessment of different surveillance systems for avian influenza in commercial poultry in Catalonia (North-Eastern Spain)

Assessment of the Current Surveillance System for Human Leptospirosis in Ecuador by Decision Analytic Modeling

Leptospirosis is a globally disseminated zoonotic disease with no national surveillance systems. On the other hand, surveillance is crucial for improving population health, and surveillance systems produce data that motivates action. Unfortunately, like many other countries, Ecuador put in place a monitoring system that has never been tested. The goal of this study was to use scenario tree modeling to assess the sensitivity of Ecuador's current national surveillance system to human leptospirosis as the basis for an economic assessment of the system. We created a decision-tree model to analyze the current system's sensitivity. The inputs were described as probabilities distributions, and the model assessed the program's sensitivity as an output. The model also considers the geographical and weather variations across Ecuador's three continental regions: Andean, Amazonia, and the Coast. Several data sources were used to create the model, including leptospirosis records from Ecuador's Ministry of Public Health, national and international literature, and expert elicitation, all of which were incorporated in a Bayesian framework. We were able to determine the most critical parameters influencing each scenario's output (CSU) sensitivity through sensitivity analysis. The Coast region had the best sensitivity scenario, with a median of 0.85% (IC 95% 0.41-0.99), followed by the Amazonia with a median of 0.54% (CI 95% 0.18-0.99) and the Andes with a median of 0.29% (CI 95% 0.02-0.89). As per the sensitivity study, the most influential criteria on the system's sensitivity were "Attendance or probability of going to a health center" and "probability of having symptoms," notably for the Coast and Amazonia Regions.

Evaluation of the sensitivity of the classical swine fever surveillance system in two free zones in Colombia

Infection with the classical swine fever virus (CSFV) causes a disease in pigs that ranges from a hyperacute form in which animals die in a few hours to subclinical disease. Due to this wide range of virulence, several complementary surveillance strategies should be implemented for the early detection of the disease. The objective of the present study was to determine the sensitivity of the surveillance system to detect CSFV outbreaks in a free zone (Zone 1) and in a zone undergoing an eradication process (Zone 2) in Colombia. Stochastic scenario tree models were used to describe the population and surveillance structures and to determine the probability of CSFV detection. The total sensitivity of the surveillance system in the case of a single infected farm in Zone 1 was 31.4% (CI 95%: 7.2-54.1) and in the case of 5 infected farms was 85.2% (CI 95%: 67.3-93.7), while in Zone 2 the sensitivities were 27.8% (CI 95%: 6.4-55.1) and 82.5% (CI 95%: 65-92.9), respectively. The on-farm passive surveillance shows the highest sensitivity for detection of a single CSFV infected farm in both zones (22.8% in Zone 1 and 22.5% in Zone 2). The probability of detection was higher in a family / backyard premise than on a commercial farm in both zones. The passive surveillance at slaughterhouse had a sensitivity of 5.3% and 4.5% for the detection of a single infected farm in Zone 1 and 2, respectively. Active surveillance presented a range of sensitivity between 2.2% and 4.5%. In conclusion, the sensitivity of the surveillance in the two studied zones was quite high, one of reasons for this good sensitivity being the sentinel network based on the voluntary participation of 5,500 collaborators that were trained for the identification and notification of diseases of national interest.

Assessment of the sensitivity of the bovine tuberculosis eradication program in a high prevalence region of Spain using scenario tree modeling

In spite of the efforts invested to eradicate bovine tuberculosis (bTB) in cattle, the disease is still present in several developed countries, including Spain. Among the factors related with disease persistence in high prevalence areas, the lack of sensitivity of the screening test for detection of infected herds [single intradermal tuberculin (SIT) test] can play a major role. Here, a scenario tree model mimicking the diagnostic test scheme in place in the region of Castilla y Leon (Spain) was developed to estimate the probability of detecting bTB in an infected-non detected officially tuberculosis free (OTF) herd (herd sensitivity, HSe). In order to do so the probability of detecting at least one positive animal in the SIT test with/without post-mortem (detection of lesions and culture) confirmation in an infected herd was estimated using Monte Carlo simulation through @RISK (Palisade Co, NY, USA). Uncertainty on the accuracy of the diagnostic tests was introduced in the model using distributions based on the literature. The performance of the model was evaluated by comparing the predicted number of SIT/post-mortem positive animals in infected herds with those observed in newly detected bTB-infected herds in the region in 2011-2015. The estimated HSe of the SIT test was 76.2% (95% probability interval: 19.8-97.6). According to the model, bTB infection would be then confirmed through culture in 65.3% (95% PI: 50.0-82.3) of the herds detected through the SIT test, so that overall the proportion of infected-non detected OTF herds in which the infection could be confirmed after the initial SIT test was 49.6% (95% PI: 9.75-80.3). The predicted HSe of both SIT test and culture was directly correlated with herd size. Results from the model suggest a moderate but highly variable HSe of the current surveillance system in place for bTB detection in OTF herds located in high prevalence areas, that could be maximized by performing multiple tests within a year as indicated in the Spanish eradication program (with a median SIT HSe of 87% when two consecutive tests were considered). In addition, these results highlight the usefulness of performing subsequent SIT tests to rule out infection in SIT-positive herds even when the causative agent cannot be isolated.

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.

Sensitivity of Bovine Tuberculosis Surveillance in Wildlife in France: A Scenario Tree Approach

Bovine tuberculosis (bTB) is a common disease in cattle and wildlife, with an impact on animal and human health, and economic implications. Infected wild animals have been detected in some European countries, and bTB reservoirs in wildlife have been identified, potentially hindering the eradication of bTB from cattle populations. However, the surveillance of bTB in wildlife involves several practical difficulties and is not currently covered by EU legislation. We report here the first assessment of the sensitivity of the bTB surveillance system for free-ranging wildlife launched in France in 2011 (the Sylvatub system), based on scenario tree modelling. Three surveillance system components were identified: (i) passive scanning surveillance for hunted wild boar, red deer and roe deer, based on carcass examination, (ii) passive surveillance on animals found dead, moribund or with abnormal behaviour, for wild boar, red deer, roe deer and badger and (iii) active surveillance for wild boar and badger. The application of these three surveillance system components depends on the geographic risk of bTB infection in wildlife, which in turn depends on the prevalence of bTB in cattle. We estimated the effectiveness of the three components of the Sylvatub surveillance system quantitatively, for each species separately. Active surveillance and passive scanning surveillance by carcass examination were the approaches most likely to detect at least one infected animal in a population with a given design prevalence, regardless of the local risk level and species considered. The awareness of hunters, which depends on their training and the geographic risk, was found to affect surveillance sensitivity. The results obtained are relevant for hunters and veterinary authorities wishing to determine the actual efficacy of wildlife bTB surveillance as a function of geographic area and species, and could provide support for decision-making processes concerning the enhancement of surveillance strategies.

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.

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.

Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations

In this globalized world, the spread of new, exotic and re-emerging diseases has become one of the most important threats to animal production and public health. This systematic review analyses conventional and novel early detection methods applied to surveillance. In all, 125 scientific documents were considered for this study. Exotic (n = 49) and re-emerging (n = 27) diseases constituted the most frequently represented health threats. In addition, the majority of studies were related to zoonoses (n = 66). The approaches found in the review could be divided in surveillance modalities, both active (n = 23) and passive (n = 5); and tools and methodologies that support surveillance activities (n = 57). Combinations of surveillance modalities and tools (n = 40) were also found. Risk-based approaches were very common (n = 60), especially in the papers describing tools and methodologies (n = 50). The main applications, benefits and limitations of each approach were extracted from the papers. This information will be very useful for informing the development of tools to facilitate the design of cost-effective surveillance strategies. Thus, the current literature review provides key information about the advantages, disadvantages, limitations and potential application of methodologies for the early detection of new, exotic and re-emerging diseases.

Optimizing the precision of case fatality ratio estimates under the surveillance pyramid approach

In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms—the symptomatic case fatality ratio (sCFR)—is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.

Detection of HPAI H5N1 viruses in ducks sampled from live bird markets in Vietnam

In Vietnam, highly pathogenic avian influenza (HPAI) H5N1 infections in poultry often occur without concomitant clinical signs and outbreaks are not consistently reported. Live bird markets represent a convenient site for surveillance that does not rely on farmers' notifications. Two H5N1 surveys were conducted at live bird markets/slaughter points in 39 districts (five provinces) in the Red River, Mekong delta, and central Vietnam during January and May 2011. Oropharyngeal and rectal swab samples from 12 480 ducks were tested for H5N1 by reverse transcription-polymerase chain reaction in pools of five. Traders and stallholders were interviewed using standardized questionnaires; 3·3% of pools tested positive. The highest prevalence (6·6%) corresponded to the Mekong delta, and no H5N1 was detected in the two Red River provinces. The surveys identified key risk behaviours of traders and stallholders. It is recommended that market surveys are implemented over time as a tool to evaluate progress in HPAI control in Vietnam.

Evaluating surveillance strategies for the early detection of low pathogenicity avian influenza infections

In recent years, the early detection of low pathogenicity avian influenza (LPAI) viruses in poultry has become increasingly important, given their potential to mutate into highly pathogenic viruses. However, evaluations of LPAI surveillance have mainly focused on prevalence and not on the ability to act as an early warning system. We used a simulation model based on data from Italian LPAI epidemics in turkeys to evaluate different surveillance strategies in terms of their performance as early warning systems. The strategies differed in terms of sample size, sampling frequency, diagnostic tests, and whether or not active surveillance (i.e., routine laboratory testing of farms) was performed, and were also tested under different epidemiological scenarios. We compared surveillance strategies by simulating within-farm outbreaks. The output measures were the proportion of infected farms that are detected and the farm reproduction number (R_h). The first one provides an indication of the sensitivity of the surveillance system to detect within-farm infections, whereas R_h reflects the effectiveness of outbreak detection (i.e., if detection occurs soon enough to bring an epidemic under control). Increasing the sampling frequency was the most effective means of improving the timeliness of detection (i.e., it occurs earlier), whereas increasing the sample size increased the likelihood of detection. Surveillance was only effective in preventing an epidemic if actions were taken within two days of sampling. The strategies were not affected by the quality of the diagnostic test, although performing both serological and virological assays increased the sensitivity of active surveillance. Early detection of LPAI outbreaks in turkeys can be achieved by increasing the sampling frequency for active surveillance, though very frequent sampling may not be sustainable in the long term. We suggest that, when no LPAI virus is circulating yet and there is a low risk of virus introduction, a less frequent sampling approach might be admitted, provided that the surveillance is intensified as soon as the first outbreak is detected.

Optimizing early detection of avian influenza H5N1 in backyard and free-range poultry production systems in Thailand

For infectious diseases such as highly pathogenic avian influenza caused by the H5N1 virus (A/H5N1 HP), early warning system is essential. Evaluating the sensitivity of surveillance is a necessary step in ensuring an efficient and sustainable system. Stochastic scenario tree modeling was used here to assess the sensitivity of the A/H5N1 HP surveillance system in backyard and free-grazing duck farms in Thailand. The whole surveillance system for disease detection was modeled with all components and the sensitivity of each component and of the overall system was estimated. Scenarios were tested according to selection of high-risk areas, inclusion of components and sampling procedure, were tested. Nationwide passive surveillance (SSC1) and risk-based clinical X-ray (SSC2) showed a similar sensitivity level, with a median sensitivity ratio of 0.96 (95% CI 0.40-15.00). They both provide higher sensitivity than the X-ray laboratory component (SSC3). With the current surveillance design, the sensitivity of detection of the overall surveillance system when the three components are implemented, was equal to 100% for a farm level prevalence of 0.05% and 82% (95% CI 71-89%) for a level of infection of 3 farms. Findings from this study illustrate the usefulness of scenario-tree modeling to document freedom from diseases in developing countries.