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Quantifying veterinarians' beliefs on disease control and exploring the effect of new evidence: a Bayesian approach.

ABSTRACT: The clinical beliefs (expectations and demands) of veterinarians regarding herd-level strategies to control mastitis, lameness, and Johne's disease were quantified in a numerical format; 94 veterinarians working in England (UK) were randomly selected and, during interviews, a statistical technique called probabilistic elicitation was used to capture their clinical expectations as probability distributions. The results revealed that markedly different clinical expectations existed for all 3 diseases, and many pairs of veterinarians had expectations with nonoverlapping 95% Bayesian credible intervals. For example, for a 3-yr lameness intervention, the most pessimistic veterinarian was centered at an 11% population mean reduction in lameness prevalence (95% credible interval: 0-21%); the most enthusiastic veterinarian was centered at a 58% reduction (95% credible interval: 38-78%). This suggests that a major change in beliefs would be required to achieve clinical agreement. Veterinarians' clinical expectations were used as priors in Bayesian models where they were combined with synthetic data (from randomized clinical trials of different sizes) to explore the effect of new evidence on current clinical opinion. The mathematical models make predictions based on the assumption that veterinarians will update their beliefs logically. For example, for the lameness intervention, a 200-farm clinical trial that estimated a 30% mean reduction in lameness prevalence was predicted to be reasonably convincing to the most pessimist veterinarian; that is, in light of this data, they were predicted to believe there would be a 0.92 probability of exceeding the median clinical demand of this sample of veterinarians, which was a 20% mean reduction in lameness. Currently, controversy exists over the extent to which veterinarians update their beliefs logically, and further research on this is needed. This study has demonstrated that probabilistic elicitation and a Bayesian framework are useful for evaluating the diversity and strength of veterinarians' clinical beliefs. The wide variations observed have implications for designing future projects. Although many factors influence disease control, nonetheless the heterogeneity in beliefs also raises concern over the extent to which a broadly consistent approach is currently being achieved; it supports the argument for more randomized clinical trials and for national programs to control nonstatutory endemic diseases.

SUBMITTER: Higgins HM

PROVIDER: S-EPMC5490737 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Quantifying veterinarians' beliefs on disease control and exploring the effect of new evidence: a Bayesian approach.

Higgins H M HM Huxley J N JN Wapenaar W W Green M J MJ

Journal of dairy science 20140327 6

The clinical beliefs (expectations and demands) of veterinarians regarding herd-level strategies to control mastitis, lameness, and Johne's disease were quantified in a numerical format; 94 veterinarians working in England (UK) were randomly selected and, during interviews, a statistical technique called probabilistic elicitation was used to capture their clinical expectations as probability distributions. The results revealed that markedly different clinical expectations existed for all 3 disea ...[more]

PMID: 24679936

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Project description:Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11-0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the "contact ratio" to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19-0.60) in BC. We developed an R package 'covidseir' to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11-0.34]), New York (0.60 [0.43-0.74]), Washington (0.84 [0.79-0.90]) and Florida (0.86 [0.76-0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07-1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures-if sufficiently strong and robustly followed-could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.

Project description:BackgroundMany native Atlantic salmon populations have been invaded by domesticated escapees for three decades or longer. However, thus far, the cumulative level of gene-flow that has occurred from farmed to wild salmon has not been reported for any native Atlantic salmon population. The aim of the present study was to investigate temporal genetic stability in native populations, and, quantify gene-flow from farmed salmon that caused genetic changes where they were observed. This was achieved by genotyping historical and contemporary samples from 20 populations covering all of Norway with recently identified single nucleotide polymorphism markers that are collectively diagnostic for farmed and wild salmon. These analyses were combined with analysis of farmed salmon and implementation of Approximate Bayesian computation based simulations.ResultsFive of the populations displayed statistically significant temporal genetic changes. All five of these populations became more similar to a pool of farmed fish with time, strongly suggesting introgression of farmed fish as the primary cause. The remaining 15 populations displayed weak or non-significant temporal genetic changes. Estimated introgression of farmed fish ranged from 2-47% per population using approximate Bayesian computation. Thus, some populations exhibited high degrees of farmed salmon introgression while others were more or less unaffected. The observed frequency of escapees in each population was moderately correlated with estimated introgression per population R² = 0.47 P < 0.001. Genetic isolation by distance existed within the historical and contemporary data sets, however, the among-population level of divergence decreased with time.ConclusionsThis is the first study to quantify cumulative introgression of farmed salmon in any native Atlantic salmon population. The estimations demonstrate that the level of introgression has been population-specific, and that the level of introgression is not solely predicted by the frequency of escapees observed in the population. However, some populations have been strongly admixed with farmed salmon, and these data provide policy makers with unique information to address this situation.

Dataset Information

Quantifying veterinarians' beliefs on disease control and exploring the effect of new evidence: a Bayesian approach.

Publications

Quantifying veterinarians' beliefs on disease control and exploring the effect of new evidence: a Bayesian approach.

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