Heterogeneity in helminth infections: factors influencing aggregation in a simple host–parasite system

The almost universally-occurring aggregated distributions of helminth burdens in host populations have major significance for parasite population ecology and evolutionary biology, but the mechanisms generating heterogeneity remain poorly understood. For the direct life cycle monogenean Discocotyle sagittata infecting rainbow trout, Oncorhynchus mykiss, variables potentially influencing aggregation can be analysed individually. This study was based at a fish farm where every host individual becomes infected by D. sagittata during each annual transmission period. Worm burdens were examined in one trout population maintained in isolation for 9 years, exposed to self-contained transmission. After this year-on-year recruitment, prevalence was 100% with intensities 10–2628, mean 576, worms per host. Parasite distribution, amongst hosts with the same age and environmental experience, was highly aggregated with variance to mean ratio 834 and negative binomial parameter, k, 0.64. The most heavily infected 20% of fish carried around 80% of the total adult parasite population. Aggregation develops within the first weeks post-infection; hosts typically carried intensities of successive age-specific cohorts that were consistent for that individual, such that heavily-infected individuals carried high numbers of all parasite age classes. Results suggest that host factors alone, operating post-infection, are sufficient to generate strongly overdispersed parasite distributions, rather than heterogeneity in exposure and initial invasion.

Bayesian Benefits for Binomial Applications in Practice

Introduction: When it comes to the practice, and teaching, of statistics, the world has primarily focused on what is known as classical or frequentist methods, rather than Bayesian methods. Scope of the Study: This paper demonstrates some beneficial properties of Bayesian methods within the commonly practiced domain of inference by utilizing consultancy case studies, one concerning an unusual sample size question and one on the detection of mail items with high biosecurity risk material. Methods: We introduce through practical applications two more aspects of the Bayesian approach which we believe are invaluable to practitioners and instructors. Having in mind readers who may be less familiar with statistical software, we have added Excel instructions which are easy to translate for those who are familiar with any such software. Findings: These cases reflect two valuable aspects for both practitioners and instructors which are unique to the Bayesian paradigm. They are: 1. including prior information to improve inference and how to apply sensitivity analysis to this inclusion and 2. the effortless inference for functions of parameters, compared with frequentist approaches. These examples involving the binomial parameter have not been considered from this perspective before, are of significant practical value and thus benefit students and instructors of courses teaching Bayesian techniques and endeavoring to include authentic learning experiences.

Binomial Parameter Determination and Mapping for Demand Prediction: A Case Study of Bike Sharing Station Expansion Design

Quantitative approaches to estimating user demand provide a powerful tool for engineering designers. We hypothesized that estimating binomial distribution parameters n (user population size) and p (user population product affinity) from historical user data can predict demand in new situations. This approach applied to a major Bike Sharing System (BSS) expansion. BSS Operators must make key decisions when adding additional docking stations. Binomial Parameter estimation approaches are briefly discussed, followed by evidence that BSSs supply an amiable case for parameter estimation. Parameter plots reveal a continuous surface over the BSS area. These surfaces allow prediction of overall ridership levels at new station locations distinctly and more accurately from approaches currently utilized. Utilizing spearman’s Rho as a comparison benchmark, our approach yields a stronger correlation between our prediction and the observed new station utilization (rho = .830, stations = 46, p < .01) than the order implemented by the BSS operator (rho = .596, stations = 46, p < .01). Finally, this approach is mathematically straightforward, indicating potential as a mainstream BSS tool for BSS operators planning future station expansions. The results validate our approach of using current user data to determine target population characteristics, informing decisions about new design situations.

The Spatial Pattern of Aphis gossypii οn Cotton

The spatial distribution of Aphis gossypii (Glover) on cotton was studied by using Taylor’s power law, the negative binomial parameter k and the iδ-index of aggregation. Both k and iδ were related to density with curvilinear relationships and indicated that aggregation decreases as density increases up to densities of, approximately, two individuals per leaf. At the very high densities (more than three individuals per leaf) the calculated values of the two indices recognized a tendency for an increased aggregation again. A strong linear relationship between the log mean and the log variance of the population density was obtained confirming the wide applicability of Taylor’s power law. However, the established relationship (b=1.433) assumes, for the range of the observed densities, a continuous decrease in the degree of aggregation as density increases. It is suggested that the changes in the degree of aggregation throughout the season and in relation to density are related to natural mortality.

Breaking Bad in Mississippi: Do County-Level Alcohol Sale Bans Encourage Crystal Methamphetamine Production and Consumption?

If alcohol has substitutes, changes in its relative price can encourage the production and consumption of other illicit and harmful drugs. This paper considers if county-level bans on the sale of alcohol in the state of Mississippi encourage the production and consumption of crystal methamphetamine. We estimate the parameters of a drug production function in which the inputs are the density of people and firms, underscoring the importance of learning and knowledge spillovers to production and consumption. Poisson and Negative Binomial parameter estimates reveal that county-level bans on hard liquor sales; but not on beer and wine, increase the number of crystal methamphetamine labs. In the absence of such laws, there would be approximately 308 fewer crystal methamphetamine labs in the state of Mississippi. Our findings suggest that in Mississippi, which is the least healthiest state in the nation, county-level bans on hard liquor sales are not welfare improving as they encourage substitution for a drug that is potentially more harmful to individual health than alcohol.

Asymmetric Loss Functions and Sample Size Determination: A Bayesian Approach

In designing monitoring systems for public health tasks it can be important to give different weights to the cases of under- and overestimation of a binomial parameter. We show how asymmetric loss functions can be used for this aim. Bayesian interval-based approaches can be combined with these loss functions and with prior knowledge about diagnostic classification errors to determine optimal sample sizes.

A Binomial Splitting Process in Connection with Corner Parking Problems

This paper studies a special type of binomial splitting process. Such a process can be used to model a high dimensional corner parking problem as well as determining the depth of random PATRICIA (practical algorithm to retrieve information coded in alphanumeric) tries, which are a special class of digital tree data structures. The latter also has natural interpretations in terms of distinct values in independent and identically distributed geometric random variables and the occupancy problem in urn models. The corresponding distribution is marked by a logarithmic mean and a bounded variance, which is oscillating, if the binomial parameterpis not equal to ½, and asymptotic to one in the unbiased case. Also, the limiting distribution does not exist as a result of the periodic fluctuations.

A Binomial Splitting Process in Connection with Corner Parking Problems

This paper studies a special type of binomial splitting process. Such a process can be used to model a high dimensional corner parking problem as well as determining the depth of random PATRICIA (practical algorithm to retrieve information coded in alphanumeric) tries, which are a special class of digital tree data structures. The latter also has natural interpretations in terms of distinct values in independent and identically distributed geometric random variables and the occupancy problem in urn models. The corresponding distribution is marked by a logarithmic mean and a bounded variance, which is oscillating, if the binomial parameter p is not equal to ½, and asymptotic to one in the unbiased case. Also, the limiting distribution does not exist as a result of the periodic fluctuations.