Sensitivity of contact-tracing for COVID-19 in Thailand: a capture-recapture application.

BACKGROUND: We investigate the completeness of contact tracing for COVID-19 during the first wave of the COVID-19 pandemic in Thailand, from early January 2020 to 30 June 2020. METHODS: Uni-list capture-recapture models were applied to the frequency distributions of index cases to inform two questions: (1) the unobserved number of index cases with contacts, and (2) the unobserved number of index cases with secondary cases among their contacts. RESULTS: Generalized linear models (using Poisson and logistic families) did not return any significant predictor (age, sex, nationality, number of contacts per case) on the risk of transmission and hence capture-recapture models did not adjust for observed heterogeneity. Best fitting models, a zero truncated negative binomial for question 1 and zero-truncated Poisson for question 2, returned sensitivity estimates for contact tracing performance of 77.6% (95% CI = 73.75-81.54%) and 67.6% (95% CI = 53.84-81.38%), respectively. A zero-inflated negative binomial model on the distribution of index cases with secondary cases allowed the estimation of the effective reproduction number at 0.14 (95% CI = 0.09-0.22), and the overdispersion parameter at 0.1. CONCLUSION: Completeness of COVID-19 contact tracing in Thailand during the first wave appeared moderate, with around 67% of infectious transmission chains detected. Overdispersion was present suggesting that most of the index cases did not result in infectious transmission chains and the majority of transmission events stemmed from a small proportion of index cases.

Public health research priorities for WHO on COVID-19 in the South-East Asia Region: results of a prioritization survey.

BACKGROUND: Effectively addressing the coronavirus disease 2019 (COVID-19) pandemic caused by the new pathogen requires continuous generation of evidence to inform decision-making. Despite an unprecedented amount of research occurring globally, the need to identify gaps in knowledge and prioritize a research agenda that is linked to public health action is indisputable. The WHO South-East Asia Region (SEAR) is likely to have region-specific research needs. METHODS: We aimed to identify a priority research agenda for guiding the regional and national response to the COVID-19 pandemic in SEAR countries. An online, anonymous research prioritization exercise using recent WHO guidance was conducted among the technical staff of WHO's country and regional offices engaged with the national COVID-19 response during October 2020. They were each asked to contribute up to five priority research ideas across seven thematic areas. These research ideas were reviewed, consolidated and scored by a core group on six parameters: regional specificity, relevance to the COVID-19 response, feasibility within regional research capacity, time to availability for decision-making, likely impact on practice, and promoting equity and gender responsiveness. The total scores for individual suggestions were organized in descending order, and ideas in the upper tertile were considered to be of high priority. RESULTS: A total of 203 priority research ideas were received from 48 respondents, who were primarily research and emergency response focal points in country and regional offices. These were consolidated into 78 research ideas and scored. The final priority research agenda of 27 items covered all thematic areas-health system (n=10), public health interventions (n=6), disease epidemiology (n=5), socioeconomic and equity (n=3), basic sciences (n=1), clinical sciences (n=1) and pandemic preparedness (n=1). CONCLUSIONS: This exercise, a part of WHO's mandate to "shape the research agenda", can help build a research roadmap ensuring efficient use of limited resources. This prioritized research agenda can act as a catalyst for Member States to accelerate research that could impact the COVID-19 response in SEAR.

First inter-laboratory comparison of Echinococcus granulosus sensu lato diagnosis in Latin America.

OBJECTIVE: To compare the performance of polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) tests for diagnosing Echinococcus granulosus in dog feces among national reference laboratories in Argentina, Chile, Peru, and Uruguay. METHODS: National laboratories affiliated with the Ministry of Health/Agriculture of each country exchanged panels of 10 positive/negative samples obtained from their regular national surveillance programs in November 2015 - November 2016. All laboratories applied PCR; two also applied ELISA techniques. Sensitivity and specificity were determined for each laboratory and concordance of results among the laboratories was evaluated by Cohen Kappa coefficient. RESULTS: Poor concordance (3 of 10 paired comparisons had values of Kappa > 0.4), low sensitivity and specificity across all laboratories, and poor performance of both techniques in detecting E. granulosus in canine feces was demonstrated in this study. An ex-post comparison of the laboratories' test protocols showed substantial heterogeneity that could partially explain poor concordance of results. CONCLUSION: The results underscore the heterogeneity of canine echinococcosis diagnosis across the region and indicate possible sources of variability. Efforts to standardize canine echinococcosis testing must be included in the plan of action for the Regional Initiative for the Control of Cystic Echinococcosis. Future comparisons with fecal samples of known parasite load are needed.

Bayesian shared spatial-component models to combine and borrow strength across sparse disease surveillance sources.

When analyzing the geographical variations of disease risk, one common problem is data sparseness. In such a setting, we investigate the possibility of using Bayesian shared spatial component models to strengthen inference and correct for any spatially structured sources of bias, when distinct data sources on one or more related diseases are available. Specifically, we apply our models to analyze the spatial variation of risk of two forms of scrapie infection affecting sheep in Wales (UK) using three surveillance sources on each disease. We first model each disease separately from the combined data sources and then extend our approach to jointly analyze diseases and data sources. We assess the predictive performances of several nested joint models through pseudo cross-validatory predictive model checks.

Participation in One Health Networks and Involvement in the COVID-19 Pandemic Response: A Global Study.

The COVID-19 pandemic exemplifies a One Health issue at the intersection of human, animal, and environmental health that requires collaboration across sectors to manage it successfully. The global One Health community includes professionals working in many different fields including human medicine, veterinary medicine, public health, ecosystem health, and, increasingly, social sciences. The aims of this cross-sectional study were to describe the involvement of the global One Health community in COVID-19 pandemic response activities. One Health networks (OHNs) have formed globally to serve professionals with common interests in collaborative approaches. We assessed the potential association between being part of an OHN and involvement in COVID-19 response activities. Data were collected in July-August 2020 using an online questionnaire that addressed work characteristics, perceived connection to OHNs, involvement in COVID-19 pandemic response activities, and barriers and facilitators to the involvement. The sample included 1,050 respondents from 94 countries across a range of organizations and work sectors including, but not restricted to, those typically associated with a One Health approach. Sixty-four percent of survey respondents indicated involvement in pandemic response activities. Being part of an OHN was positively associated with being involved in the COVID-19 response (odds ratio: 1.8, 95% confidence interval: 1.3-2.4). Lack of opportunities was a commonly reported barrier to involvement globally, with lack of funding the largest barrier in the WHO African region. This insight into diverse workforce involvement in the pandemic helps fill a gap in the global health workforce and public health education literature. An expanded understanding of the perceived roles and value of OHNs can inform targeted interventions to improve public health education and workforce capacity to prepare for and respond to public health emergencies.

The Relative Importance of Vulnerability and Efficiency in COVID-19 Contact Tracing Programmes: A Discrete Choice Experiment.

Objectives: This study aims to assess the trade-offs between vulnerability and efficiency attributes of contact tracing programmes based on preferences of COVID-19 contact tracing practitioners, researchers and other relevant stakeholders at the global level. Methods: We conducted an online discrete choice experiment (DCE). Respondents were recruited globally to explore preferences according to country income level and the prevailing epidemiology of COVID-19 in the local setting. The DCE attributes represented efficiency (timeliness, completeness, number of contacts), vulnerability (vulnerable population), cooperation and privacy. A mixed-logit model and latent class analysis were used. Results: The number of respondents was 181. Timeliness was the most important attribute regardless of country income level and COVID-19 epidemiological condition. Vulnerability of contacts was the second most important attribute for low-to-lower-middle-income countries and third for upper-middle-to-high income countries. When normalised against conditional relative importance of timeliness, conditional relative importance of vulnerability ranged from 0.38 to 0.42. Conclusion: Vulnerability and efficiency criteria were both considered to be important attributes of contact tracing programmes. However, the relative values placed on these criteria varied significantly between epidemiological and economic context.

Antimicrobial resistance control efforts in Africa: a survey of the role of Civil Society Organisations.

Background: Antimicrobial resistance (AMR) is a growing public health threat in Africa. AMR prevention and control requires coordination across multiple sectors of government and civil society partners. Objectives: To assess the current role, needs, and capacities of CSOs working in AMR in Africa. Methods: We conducted an online survey of 35 CSOs working in 37 countries across Africa. The survey asked about priorities for AMR, current AMR-specific activities, monitoring practices, training needs, and preferences for sharing information on AMR. Further data were gathered on the main roles of the organisations, the length of time engaged in and budget spent on AMR-related activities, and their involvement in the development and implementation of National Action Plans (NAPs). Results were assessed against The Africa Centres for Disease Control and Prevention (Africa CDC) Framework for Antimicrobial Resistance (2018-2023). Results: CSOs with AMR-related activities are working in all four areas of Africa CDC's Framework: improving surveillance, delaying emergence, limiting transmission, and mitigating harm from infections caused by AMR microorganisms. Engagement with the four objectives is mainly through advocacy, followed by accountability and service delivery. There were limited monitoring activities reported by CSOs, with only seven (20%) providing an example metric used to monitor their activities related to AMR, and 27 (80%) CSOs reporting having no AMR-related strategy. Half the CSOs reported engaging with the development and implementation of NAPs; however, only three CSOs are aligning their work with these national strategies. Conclusion: CSOs across Africa are supporting AMR prevention and control, however, there is potential for more engagement. Africa CDC and other government agencies should support the training of CSOs in strategies to control AMR. Tailored training programmes can build knowledge of AMR, capacity for monitoring processes, and facilitate further identification of CSOs' contribution to the AMR Framework and alignment with NAPs and regional strategies.

Antimicrobial practices among small animal veterinarians in Greece: a survey.

BACKGROUND: The inappropriate use of antibiotics is a major issue in clinical practice in Greece with serious implications for public health and animal health. The purpose of the present study was to provide a first insight into the use of antibiotics by small animal practitioners in Greece and assess their compliance with general rules for the rational use of antibiotics. This is the first survey of its kind in Greece. METHODS: A questionnaire was designed to collect basic information on the use of antibiotics by pet veterinarians. The questionnaire was sent to a total of 70 veterinarians mainly operating in the region of Attica, a region that comprises almost 50% of the Greek population and where veterinarians are engaged solely in small animal practice. The questionnaire consisted of 37 closed questions dealing with various aspects on the use of antibiotics. RESULTS: The majority of practitioners report cases where the pet owner initiated antibiotic treatment without veterinary prescription. Almost every clinician reported owner-compliance challenges. Regarding microbiological analysis, 73% of respondents initiate empirical treatment while waiting for laboratory results or use antibiogram only when the treatment is unsuccessful. Eighty-eight per cent declared to use antimicrobials postoperatively in clean surgical procedures. Different types of antimicrobials and treatment durations than the ones proposed by guidelines on rational use of antibiotics are preferred for various organ systems e.g. in urinary and gastrointestinal infections. CONCLUSIONS: Our findings suggest the need for guidelines on antibiotic use in small animal practice in Greece, and the deployment of systematic surveillance on antimicrobials use and resistance to inform the initial choice of antibiotics upon local antimicrobial resistance profiles. Targeting the other end of the problem, pet owners, our findings indicate the need to educate them on the rational use of antibiotics and, critically, stop antibiotic availability without prescription.

Integration of animal health and public health surveillance sources to exhaustively inform the risk of zoonosis: An application to echinococcosis in Rio Negro, Argentina.

The analysis of zoonotic disease risk requires the consideration of both human and animal geo-referenced disease incidence data. Here we show an application of joint Bayesian analyses to the study of echinococcosis granulosus (EG) in the province of Rio Negro, Argentina. We focus on merging passive and active surveillance data sources of animal and human EG cases using joint Bayesian spatial and spatio-temporal models. While similar spatial clustering and temporal trending was apparent, there appears to be limited lagged dependence between animal and human outcomes. Beyond the data quality issues relating to missingness at different times, we were able to identify relations between dog and human data and the highest 'at risk' areas for echinococcosis within the province.

On the question of proportionality of the count of observed scrapie cases and the size of holding.

BACKGROUND: The present paper investigates the question of a suitable basic model for the number of scrapie cases in a holding and applications of this knowledge to the estimation of scrapie-affected holding population sizes and adequacy of control measures within holding. Is the number of scrapie cases proportional to the size of the holding in which case it should be incorporated into the parameter of the error distribution for the scrapie counts? Or, is there a different - potentially more complex - relationship between case count and holding size in which case the information about the size of the holding should be better incorporated as a covariate in the modeling? METHODS: We show that this question can be appropriately addressed via a simple zero-truncated Poisson model in which the hypothesis of proportionality enters as a special offset-model. Model comparisons can be achieved by means of likelihood ratio testing. The procedure is illustrated by means of surveillance data on classical scrapie in Great Britain. Furthermore, the model with the best fit is used to estimate the size of the scrapie-affected holding population in Great Britain by means of two capture-recapture estimators: the Poisson estimator and the generalized Zelterman estimator. RESULTS: No evidence could be found for the hypothesis of proportionality. In fact, there is some evidence that this relationship follows a curved line which increases for small holdings up to a maximum after which it declines again. Furthermore, it is pointed out how crucial the correct model choice is when applied to capture-recapture estimation on the basis of zero-truncated Poisson models as well as on the basis of the generalized Zelterman estimator. Estimators based on the proportionality model return very different and unreasonable estimates for the population sizes. CONCLUSION: Our results stress the importance of an adequate modelling approach to the association between holding size and the number of cases of classical scrapie within holding. Reporting artefacts and speculative biological effects are hypothesized as the underlying causes of the observed curved relationship. The lack of adjustment for these artefacts might well render ineffective the current strategies for the control of the disease.

Within-holding prevalence of sheep classical scrapie in Great Britain.

BACKGROUND: Data from the Compulsory Scrapie Flocks Scheme (CSFS), part of the compulsory eradication measures for the control of scrapie in the EU, have been used to estimate the within-holding prevalence of classical scrapie in Great Britain (GB). Specifically data from one of the testing routes within the CSFS have been used; the initial cull (IC), whereby two options can be applied: the whole flock cull option by which the entire flock is depopulated, and the genotyping and cull of certain genotypes. RESULTS: Between April 2005 and September 2007, 25,316 suitable samples, submitted from 411 flocks in 213 scrapie-affected holdings in Great Britain, were tested for scrapie. The predicted within-holding prevalence for the initial cull was 0.65% (95% CI: 0.55-0.75). For the whole cull option was 0.47% (95% CI: 0.32-0.68) and for the genotype and cull or mixed option (both options applied in different flocks of the same holding), the predicted within-holding prevalence was 0.7% (95% CI: 0.6-0.83). There were no significant differences in the within-flock prevalence between countries (England, Scotland and Wales) or between CSFS holdings by the surveillance stream that detected the index case. The number of CSFS flocks on a holding did not affect the overall within-holding prevalence of classical scrapie. CONCLUSION: These estimates are important in the discussion of the epidemiological implications of the current EU testing programme of scrapie-affected flocks and to inform epidemiological and mathematical models. Furthermore, these estimates may provide baseline data to assist the design of future surveillance activities and control policies with the aim to increase their efficiency.

Classical sheep scrapie in Great Britain: spatial analysis and identification of environmental and farm-related risk factors.

BACKGROUND: Previous studies suggest that the spatial distribution of classical sheep scrapie in Great Britain is uneven and that certain flock characteristics may be associated with occurrence of the disease. However, the existence of areas of high and low disease-risk may also result from differences in the spatial distribution of environmental characteristics. In this study we explored the spatial pattern of classical scrapie in Great Britain between 2002 and 2005 and investigated the association between disease occurrence and various environmental and farm-related risk factors. RESULTS: Exploratory spatial analysis: South Wales was found to have a higher density of scrapie-positive farms than the rest of Great Britain. In addition, a small cluster of high-risk farms was identified in the center of this region in which clustering of scrapie-positive farms occurred up to a distance of approximately 40 km. SPATIAL MODELLING: A mixed-effects regression model identified flock-size and soil drainage to be significantly associated with the occurrence of scrapie in England and Wales (area under the curve (AUC) 0.71 +/- 0.01, 95% CI 0.68 - 0.74). The predictive risk map based on the estimated association between these factors and disease occurrence showed most of Wales to be at risk of being confirmed positive for scrapie with areas of highest risk in central and south Wales. In England, areas with the highest risk occurred mainly in the north and the midlands. CONCLUSION: The observed distribution of scrapie in Great Britain exhibited a definite spatial pattern with south Wales identified as an area of high occurrence. In addition both flock (flock size) and environmental variables (soil drainage) were found to be significantly associated with the occurrence of the disease. However, the model's AUC indicated unexplained variation remaining in the model and the source of this variation may lie in farm-level characteristics rather than spatially-varying ones such as environmental factors.

Spatial distribution of the active surveillance of sheep scrapie in Great Britain: an exploratory analysis.

BACKGROUND: This paper explores the spatial distribution of sampling within the active surveillance of sheep scrapie in Great Britain. We investigated the geographic distribution of the birth holdings of sheep sampled for scrapie during 2002 - 2005, including samples taken in abattoir surveys (c. 83,100) and from sheep that died in the field ("fallen stock", c. 14,600). We mapped the birth holdings by county and calculated the sampling rate, defined as the proportion of the holdings in each county sampled by the surveys. The Moran index was used to estimate the global spatial autocorrelation across Great Britain. The contributions of each county to the global Moran index were analysed by a local indicator of spatial autocorrelation (LISA). RESULTS: The sampling rate differed among counties in both surveys, which affected the distribution of detected cases of scrapie. Within each survey, the county sampling rates in different years were positively correlated during 2002-2005, with the abattoir survey being more strongly autocorrelated through time than the fallen stock survey. In the abattoir survey, spatial indices indicated that sampling rates in neighbouring counties tended to be similar, with few significant contrasts. Sampling rates were strongly correlated with sheep density, being highest in Wales, Southwest England and Northern England. This relationship with sheep density accounted for over 80% of the variation in sampling rate among counties. In the fallen stock survey, sampling rates in neighbouring counties tended to be different, with more statistically significant contrasts. The fallen stock survey also included a larger proportion of holdings providing many samples. CONCLUSION: Sampling will continue to be uneven unless action is taken to make it more uniform, if more uniform sampling becomes a target. Alternatively, analyses of scrapie occurrence in these datasets can take account of the distribution of sampling. Combining the surveys only partially reduces uneven sampling. Adjusting the distribution of sampling between abattoirs to reduce the bias in favour of regions with high sheep densities could probably achieve more even sampling. However, any adjustment of sampling should take account of the current understanding of the distribution of scrapie cases, which will be improved by further analysis of this dataset.

Assessment of Area-Level Disease Control and Surveillance Vulnerabilities: An Application to Visceral Leishmaniasis in Brazil.

The large number of activities contributing to zoonoses surveillance and control capability, on both human and animal domains, and their likely heterogeneous implementation across administrative units make assessment and comparisons of capability performance between such units a complex task. Such comparisons are important to identify gaps in capability development, which could lead to clusters of vulnerable areas, and to rank and subsequently prioritize resource allocation toward the least capable administrative units. Area-level preparedness is a multidimensional entity and, to the best of our knowledge, there is no consensus on a single comprehensive indicator, or combination of indicators, in a summary metric. We use Bayesian spatial factor analysis models to jointly estimate and rank disease control and surveillance capabilities against visceral leishmaniasis (VL) at the municipality level in Brazil. The latent level of joint capability is informed by four variables at each municipality, three reflecting efforts to monitor and control the disease in humans, and one variable informing surveillance capability on the reservoir, the domestic dog. Because of the large volume of missing data, we applied imputation techniques to allow production of comprehensive rankings. We were able to show the application of these models to this sparse dataset and present a ranked list of municipalities based on their overall VL capability. We discuss improvements to our models, and additional applications.

No temporal trends in the prevalence of atypical scrapie in British sheep, 2002-2006.

BACKGROUND: So-called atypical scrapie was first identified in Great Britain (GB) in 2002 following the introduction of wide-scale scrapie surveillance. In particular, abattoir and fallen stock surveys have been carried out in GB since 2002, with a total of 147 atypical positives identified by the end of 2006. The results of these surveys provide data with which to assess temporal trends in the prevalence of atypical scrapie in sheep in Great Britain between 2002 and 2006. RESULTS: Using the results of abattoir and fallen stock surveys, the prevalence of atypical scrapie (percentage of samples positive) was estimated. The prevalence in the abattoir and fallen stock surveys, for all years combined, was 0.09% (95% confidence interval (CI): 0.08%-0.11%) and 0.07% (95% CI: 0.05%-0.11%), respectively. There were no significant temporal trends in either survey. Comparing the surveys' results, there were no significant differences in annual prevalence or the prevalence within PrP genotypes. For the abattoir survey, the PrP genotype with the highest prevalence was AHQ/AHQ, which was significantly higher than all other genotypes, except ARR/AHQ, AHQ/ARH and ARH/ARQ. CONCLUSION: The estimated prevalence of atypical scrapie was similar in both the abattoir and fallen stock surveys. Our results indicate there was no significant temporal trend in prevalence, adding to evidence that this atypical form of scrapie may be a sporadic condition or, if it is infectious, that the force of infection is very low.

A bagging-based correction for the mixture model estimator of population size.

Estimation of a population size by means of capture-recapture techniques is an important problem occurring in many areas of life and social sciences. We consider the frequencies of frequencies situation, where a count variable is used to summarize how often a unit has been identified in the target population of interest. The distribution of this count variable is zero-truncated since zero identifications do not occur in the sample. As an application we consider the surveillance of scrapie in Great Britain. In this case study holdings with scrapie that are not identified (zero counts) do not enter the surveillance database. The count variable of interest is the number of scrapie cases per holding. For count distributions a common model is the Poisson distribution and, to adjust for potential heterogeneity, a discrete mixture of Poisson distributions is used. Mixtures of Poissons usually provide an excellent fit as will be demonstrated in the application of interest. However, as it has been recently demonstrated, mixtures also suffer under the so-called boundary problem, resulting in overestimation of population size. It is suggested here to select the mixture model on the basis of the Bayesian Information Criterion. This strategy is further refined by employing a bagging procedure leading to a series of estimates of population size. Using the median of this series, highly influential size estimates are avoided. In limited simulation studies it is shown that the procedure leads to estimates with remarkable small bias.

Extending Zelterman's approach for robust estimation of population size to zero-truncated clustered Data.

Estimation of population size with missing zero-class is an important problem that is encountered in epidemiological assessment studies. Fitting a Poisson model to the observed data by the method of maximum likelihood and estimation of the population size based on this fit is an approach that has been widely used for this purpose. In practice, however, the Poisson assumption is seldom satisfied. Zelterman (1988) has proposed a robust estimator for unclustered data that works well in a wide class of distributions applicable for count data. In the work presented here, we extend this estimator to clustered data. The estimator requires fitting a zero-truncated homogeneous Poisson model by maximum likelihood and thereby using a Horvitz-Thompson estimator of population size. This was found to work well, when the data follow the hypothesized homogeneous Poisson model. However, when the true distribution deviates from the hypothesized model, the population size was found to be underestimated. In the search of a more robust estimator, we focused on three models that use all clusters with exactly one case, those clusters with exactly two cases and those with exactly three cases to estimate the probability of the zero-class and thereby use data collected on all the clusters in the Horvitz-Thompson estimator of population size. Loss in efficiency associated with gain in robustness was examined based on a simulation study. As a trade-off between gain in robustness and loss in efficiency, the model that uses data collected on clusters with at most three cases to estimate the probability of the zero-class was found to be preferred in general. In applications, we recommend obtaining estimates from all three models and making a choice considering the estimates from the three models, robustness and the loss in efficiency.

Rabies in the Americas: 1998-2014.

Through national efforts and regional cooperation under the umbrella of the Regional Program for the Elimination of Rabies, dog and human rabies have decreased significantly in Latin America and Caribbean (LAC) countries over the last three decades. To achieve this decline, LAC countries had to develop national plans, and consolidate capabilities such as regular mass dog vaccination, opportune post-exposure prophylaxis and sensitive surveillance. This paper presents longitudinal data for 21 LAC countries on dog vaccination, PEP and rabies surveillance collected from the biannual regional meeting for rabies directors from 1998-2014 and from the Regional Epidemiologic Surveillance System for Rabies (SIRVERA). Differences in human and dog rabies incidence rates and dog vaccination rates were shown between low, middle and high-income countries. At the peak, over 50 million dogs were vaccinated annually in national campaigns in the countries represented. The reported number of animal exposures remained fairly stable during the study period with an incidence rate ranging from 123 to 191 reported exposures per 100,000 people. On average, over 2 million doses of human vaccine were applied annually. In the most recent survey, only 37% of countries reported that they had sufficient financial resources to meet the program objectives. The data show a sufficient and sustained effort of the LAC countries in the area of dog vaccination and provide understanding of the baseline effort required to reduce dog-mediated rabies incidence.

Explaining the heterogeneous scrapie surveillance figures across Europe: a meta-regression approach.

BACKGROUND: Two annual surveys, the abattoir and the fallen stock, monitor the presence of scrapie across Europe. A simple comparison between the prevalence estimates in different countries reveals that, in 2003, the abattoir survey appears to detect more scrapie in some countries. This is contrary to evidence suggesting the greater ability of the fallen stock survey to detect the disease. We applied meta-analysis techniques to study this apparent heterogeneity in the behaviour of the surveys across Europe. Furthermore, we conducted a meta-regression analysis to assess the effect of country-specific characteristics on the variability. We have chosen the odds ratios between the two surveys to inform the underlying relationship between them and to allow comparisons between the countries under the meta-regression framework. Baseline risks, those of the slaughtered populations across Europe, and country-specific covariates, available from the European Commission Report, were inputted in the model to explain the heterogeneity. RESULTS: Our results show the presence of significant heterogeneity in the odds ratios between countries and no reduction in the variability after adjustment for the different risks in the baseline populations. Three countries contributed the most to the overall heterogeneity: Germany, Ireland and The Netherlands. The inclusion of country-specific covariates did not, in general, reduce the variability except for one variable: the proportion of the total adult sheep population sampled as fallen stock by each country. A large residual heterogeneity remained in the model indicating the presence of substantial effect variability between countries. CONCLUSION: The meta-analysis approach was useful to assess the level of heterogeneity in the implementation of the surveys and to explore the reasons for the variation between countries.

Diagnosing scrapie in sheep: a classification experiment.

Scrapie is a neuro-degenerative disease in small ruminants. A data set of 3113 records of sheep reported to the Scrapie Notifications Database in Great Britain has been studied. Clinical signs were recorded as present/absent in each animal by veterinary officials (VO) and a post-mortem diagnosis was made. In an attempt to detect healthy animals within the set of suspects using only the clinical signs, 18 classification methods were applied ranging from simple linear classifiers to classifier ensembles such as Bagging, AdaBoost and Random Forests. The results suggest that the clinical classification by the VO was adequate as no further differentiation within the set of suspects was feasible.