Estimating herd prevalence on the basis of aggregate testing of animals (original) (raw)
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Evaluating the health status of herds based on tests applied to individuals
Preventive Veterinary Medicine, 1992
Martin, S.W., Shoukri, M. and Thorburn, M.A., 1992. Evaluating the health status of herds based on tests applied to individuals. Prey. Vet. Med., The effects of test sensitivity and specificity, and the impact of true prevalence of disease, on test results at the individual level are well known. When individuals are tested to ascertain if an aggregate of animals (e.g. a herd) is affected by a condition of interest, the number of animals tested and the critical number of reactors used to decide the health status of the herd become very important in influencing the herd-level sensitivity and specificity.
A practical approach to calculate sample size for herd prevalence surveys
Preventive Veterinary Medicine, 2004
When designing a herd-level prevalence study that will use an imperfect diagnostic test, it is necessary to consider the test sensitivity and specificity. A new approach was developed to take into account the imperfections of the test. We present an adapted formula that, when combined with an existing piece of software, allows improved planning. Bovine paratuberculosis is included as an example infection because it originally stimulated the work. Examples are provided of the trade-off between the benefit (low number of herds) and the disadvantage (large number of animals per herd and exclusion of small herds) that are associated with achieving high herd-level sensitivity and specificity. We demonstrate the bias in the estimate of prevalence and the underestimate of the confidence range that would arise if we did not account for test sensitivity and specificity. #
Determining the infection status of a herd
Journal of Agricultural, Biological, and Environmental Statistics, 2003
This article presents hierarchical models for determining infection status and prevalence of infection within a herd given a hypergeometric or binomial sample of animals that have been screened with an imperfect test. Expert prior information on the infection status of the herd, diagnostic test accuracy, and herd prevalence is incorporated into the model. Posterior probabilities versus prior probabilities of infection are presented in the novel form of a curve, summarizing the probability of infection over a range of possible prior probability values. We demonstrate the model with serologic data for Mycobacterium paratuberculosis(Johne's disease) in dairy herds.
Use of pooled serum samples to assess herd disease status using commercially available ELISAs
Tropical Animal Health and Production
Pooled samples are used in veterinary and human medicine as a cost-effective approach to monitor disease prevalence. Nonetheless, there is limited information on the effect of pooling on test performance, and research is required to determine the appropriate number of samples which can be pooled. Therefore, this study aimed to evaluate the use of pooled serum samples as a herd-level surveillance tool for infectious production-limiting diseases: bovine viral diarrhoea (BVD), infectious bovine rhinotracheitis (IBR), enzootic bovine leukosis (EBL) and Neospora caninum (NC), by investigating the maximum number of samples one can pool to identify one positive animal, using commercial antibody-detection ELISAs. Four positive field standards (PFS), one for each disease, were prepared by pooling highly positive herd-level samples diagnosed using commercially available ELISA tests. These PFS were used to simulate 18 pooled samples ranging from undiluted PFS to a dilution representing 1 posit...
Decision Making Based on Sampled Disease Occurrence in Animal Herds
Lecture Notes in Computer Science, 2003
To make qualified decisions when extrapolating results from a survey sample with imprecise tests requires careful handling of uncertainty. Both the imprecise test and uncertainty introduced by the sampling have to be taken into account in order to act optimally. This paper formulates an influence diagram with discrete and continuous nodes to handle an example typical for animal production: a veterinarian whoas part of a biosecurity program -has to decide whether to treat a herd of animals after inspecting a small fraction of them. Our aim is to investigate the robustness of the obtained strategy by performing a two-way sensitivity analysis with respect to the proportion of false positives and false negatives of the test. Output of the analysis is a treatment map illustrating how the chosen strategy varies according to variation in these proportions. The map helps to investigate whether a certain variation is acceptable or if the test procedure has to be standardized in order to reduce variation. Objective of the paper is to be an appetizer to work more with the issues raised in obtaining a practical solution.
Preventive veterinary medicine, 2014
In the design of surveillance, there is often a desire to target high risk herds. Such risk-based approaches result in better allocation of resources and improve the performance of surveillance activities. For many contagious animal diseases, movement of live animals is a main route of transmission, and because of this, herds that purchase many live animals or have a large contact network due to trade can be seen as a high risk stratum of the population. This paper presents a new method to assess herd disease risk in animal movement networks. It is an improvement to current network measures that takes direction, temporal order, and also movement size and probability of disease into account. In the study, the method was used to calculate a probability of disease ratio (PDR) of herds in simulated datasets, and of real herds based on animal movement data from dairy herds included in a bulk milk survey for Coxiella burnetii. Known differences in probability of disease are easily incorpo...
Methods in Ecology and Evolution, 2012
1. Reliable assessments of infection status and population prevalence are critical for epidemiological modelling and disease management, but can be greatly biased when disease state is determined from imperfect diagnostic tests. Available statistical methods to adjust test-based prevalence estimates by correcting for test accuracy demand that many stringent requirements and assumptions be met (knowledge about underlying population prevalence or multiple diagnostic methods), limiting their utility for wildlife disease surveys. 2. In this paper, we present site-occupancy modelling as a flexible approach to derive estimates of population prevalence and test sensitivity under imperfect pathogen detection without a need for restrictive requirements or assumptions. We extend the utility of the standard site-occupancy framework for pathogen detection data by novel application of abundance-induced heterogeneity (AIH) models ) that allow test sensitivity to vary with host pathogen load or infection intensity. 3. We demonstrate the utility of this approach for wildlife disease studies by applying site-occupancy models to a data set consisting of replicate quantitative (q)PCR diagnoses of malaria parasites (Plasmodium spp.) in blood samples from wild blue tits (Cyanistes caeruleus). 4. Model selection revealed that Plasmodium detection rates by qPCR were strongly dependent on host parasite load. Estimates of parasite detection rates revealed the qPCR assay to be highly sensitive, with accordingly, a very low probability of false negative diagnosis for the majority of infected hosts in our population and little bias in naive estimates of population prevalence, although this will be a system-specific result. 5. Our results demonstrate the utility of a site-occupancy approach for deriving estimates of population prevalence under imperfect pathogen detection and reveal that accounting for host variation in pathogen load allows a more accurate assessment of diagnostic test sensitivity. 6. By identifying factors that influence pathogen detection rates, and revealing optimal protocols for obtaining unbiased prevalence estimates, while minimising the probability of false negative diagnoses, we also show that this approach can enhance both diagnostic accuracy and cost-efficiency in wildlife disease surveys.