Mapping Brucellosis Increases Relative to Elk Density Using Hierarchical Bayesian Models

The relationship between host density and parasite transmission is central to the effectiveness of many disease management strategies. Few studies, however, have empirically estimated this relationship particularly in large mammals. We applied hierarchical Bayesian methods to a 19-year dataset of over 6400 brucellosis tests of adult female elk (Cervus elaphus) in northwestern Wyoming. Management captures that occurred from January to March were over two times more likely to be seropositive than hunted elk that were killed in September to December, while accounting for site and year effects. Areas with supplemental feeding grounds for elk had higher seroprevalence in 1991 than other regions, but by 2009 many areas distant from the feeding grounds were of comparable seroprevalence. The increases in brucellosis seroprevalence were correlated with elk densities at the elk management unit, or hunt area, scale (mean 2070 km²; range  = [95–10237]). The data, however, could not differentiate among linear and non-linear effects of host density. Therefore, control efforts that focus on reducing elk densities at a broad spatial scale were only weakly supported. Additional research on how a few, large groups within a region may be driving disease dynamics is needed for more targeted and effective management interventions. Brucellosis appears to be expanding its range into new regions and elk populations, which is likely to further complicate the United States brucellosis eradication program. This study is an example of how the dynamics of host populations can affect their ability to serve as disease reservoirs.     

Quantifying Inter- and Intra-Population Niche Variability Using Hierarchical Bayesian Stable Isotope Mixing Models

Variability in resource use defines the width of a trophic niche occupied by a population. Intra-population variability in resource use may occur across hierarchical levels of population structure from individuals to subpopulations. Understanding how levels of population organization contribute to population niche width is critical to ecology and evolution. Here we describe a hierarchical stable isotope mixing model that can simultaneously estimate both the prey composition of a consumer diet and the diet variability among individuals and across levels of population organization. By explicitly estimating variance components for multiple scales, the model can deconstruct the niche width of a consumer population into relevant levels of population structure. We apply this new approach to stable isotope data from a population of gray wolves from coastal British Columbia, and show support for extensive intra-population niche variability among individuals, social groups, and geographically isolated subpopulations. The analytic method we describe improves mixing models by accounting for diet variability, and improves isotope niche width analysis by quantitatively assessing the contribution of levels of organization to the niche width of a population.     

A Bayesian Network View on Nested Effects Models

Nested effects models (NEMs) are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in the /Bioconductor package nem. We validate these methods in a simulation study and apply them to a synthetic lethality dataset in yeast.     

An Experimental Approach to the Effects of Predictability on Community Structure

This paper considers the effects of different levels of resourcepredictability on niche widths, competition and diversity ina community of three hummingbird species. Three pairs of hypothesesare tested: whether decreasing predictability (a) increasesor decreases niche width, (b) increases or decreases competitionand (c) increases or decreases diversity. The results show thatcomplementary increases and decreases in niche width occur withdecreasing predictability, while competition and diversity decrease,at least with extreme unpredictability. A model, which assumesthe species to have similar resource preferences, and its predictionsare examined. The dominant species, Lampornis clemenciae, excludes a subordinatespecies, Archilochus alexandri, from preferred resources. Thisdefense becomes unprofitable with decreasing predictabilityand Archilochus invades the resources once vigorously defendedby Lampornis. When the spectrum of resources is altered in thedirection of decreasing suitability for both species, Lampornisbecomes even more specialized and sensitive to the effects ofunpredictability. A third species, Eugenes fulgens, steals resourcesundefended by Lampornis. These characteristics seem to be commonto a number of communities in very different taxonomic groups,and characteristic of communities in which species share a commonpreferred resource. The question of how a community organizedwith distinct resource preferences responds to decreasing predictabilityremains open.     

Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

Modern high-throughput biotechnologies such as microarray and next-generation sequencing produce a massive amount of information for each sample assayed. However, in a typical high-throughput experiment, only limited amount of data are observed for each individual feature, thus the classical “large p, small n” problem. Bayesian hierarchical model, capable of borrowing strength across features within the same dataset, has been recognized as an effective tool in analyzing such data. However, the shrinkage effect, the most prominent feature of hierarchical features, can lead to undesirable over-correction for some features. In this work, we discuss possible causes of the over-correction problem and propose several alternative solutions. Our strategy is rooted in the fact that in the Big Data era, large amount of historical data are available which should be taken advantage of. Our strategy presents a new framework to enhance the Bayesian hierarchical model. Through simulation and real data analysis, we demonstrated superior performance of the proposed strategy. Our new strategy also enables borrowing information across different platforms which could be extremely useful with emergence of new technologies and accumulation of data from different platforms in the Big Data era. Our method has been implemented in R package “adaptiveHM,” which is freely available from https://github.com/benliemory/adaptiveHM.     

Bayesian Hierarchical Pattern Mixture Models for Comparative Effectiveness of Drugs and Drug Classes Using Healthcare Data: A Case Study Involving Antihypertensive Medications

Comparative effectiveness research aims, in part, to provide evidence most relevant to clinical decision making. One decision relevant to hypertensive patients is which therapeutic drug class is the most safe and effective. In addition, once a drug class has been chosen it would be useful to know whether there are differences in effectiveness between drugs within class. Randomized trials are unlikely to provide sufficient evidence for answering these questions. We therefore propose a modeling approach that can be used to address the questions using administrative databases. We propose a Bayesian hierarchical model, where drugs are nested within their corresponding class. We account for the type of missing data that are common in these databases using a pattern mixture model. The methodology is illustrated using data from a comparative effectiveness study of antihypertensive medications.     

Two-Stage Least Squares Analysis of Hierarchical Linear Models

This paper has two major objectives. The first is to present a two-stage least squares procedure for estimation of the parameters in a linear model whose parameters are in themselves linear functions of some hyperparameters. The second, and perhaps more important point, is that the new estimator can be shown to be generally more precise than either the Bayesian or the generalized single-stage least squares estimator reported by LINDLEY and SMITH (1072).     

Bayesian Inference of Initial Models in Cryo-Electron Microscopy Using Pseudo-atoms

Single-particle cryo-electron microscopy is widely used to study the structure of macromolecular assemblies. Tens of thousands of noisy two-dimensional images of the macromolecular assembly viewed from different directions are used to infer its three-dimensional structure. The first step is to estimate a low-resolution initial model and initial image orientations. This is a challenging global optimization problem with many unknowns, including an unknown orientation for each two-dimensional image. Obtaining a good initial model is crucial for the success of the subsequent refinement step. We introduce a probabilistic algorithm for estimating an initial model. The algorithm is fast, has very few algorithmic parameters, and yields information about the precision of estimated model parameters in addition to the parameters themselves. Our algorithm uses a pseudo-atomic model to represent the low-resolution three-dimensional structure, with isotropic Gaussian components as moveable pseudo-atoms. This leads to a significant reduction in the number of parameters needed to represent the three-dimensional structure, and a simplified way of computing two-dimensional projections. It also contributes to the speed of the algorithm. We combine the estimation of the unknown three-dimensional structure and image orientations in a Bayesian framework. This ensures that there are very few parameters to set, and specifies how to combine different types of prior information about the structure with the given data in a systematic way. To estimate the model parameters we use Markov chain Monte Carlo sampling. The advantage is that instead of just obtaining point estimates of model parameters, we obtain an ensemble of models revealing the precision of the estimated parameters. We demonstrate the algorithm on both simulated and real data.     

Modelling the Dynamics of an Experimental Host-Pathogen Microcosm within a Hierarchical Bayesian Framework

The advantages of Bayesian statistical approaches, such as flexibility and the ability to acknowledge uncertainty in all parameters, have made them the prevailing method for analysing the spread of infectious diseases in human or animal populations. We introduce a Bayesian approach to experimental host-pathogen systems that shares these attractive features. Since uncertainty in all parameters is acknowledged, existing information can be accounted for through prior distributions, rather than through fixing some parameter values. The non-linear dynamics, multi-factorial design, multiple measurements of responses over time and sampling error that are typical features of experimental host-pathogen systems can also be naturally incorporated. We analyse the dynamics of the free-living protozoan Paramecium caudatum and its specialist bacterial parasite Holospora undulata. Our analysis provides strong evidence for a saturable infection function, and we were able to reproduce the two waves of infection apparent in the data by separating the initial inoculum from the parasites released after the first cycle of infection. In addition, the parameter estimates from the hierarchical model can be combined to infer variations in the parasite''s basic reproductive ratio across experimental groups, enabling us to make predictions about the effect of resources and host genotype on the ability of the parasite to spread. Even though the high level of variability between replicates limited the resolution of the results, this Bayesian framework has strong potential to be used more widely in experimental ecology.     

Quantifying Hypoxia Impacts on an Estuarine Demersal Community Using a Hierarchical Ensemble Approach

In coastal marine ecosystems, hypoxia and anoxia are emerging as growing threats whose ecological impacts are difficult to ascertain because of the frequent lack of adequate references for comparison. We applied conventional and hierarchical ensemble analyses to evaluate the weight of evidence in support of hypoxia impacts on local densities of individual and groups of demersal fish and invertebrate species in Hood Canal, WA, which is subject to seasonal hypoxia in its southern reaches. Central to our approach was a sample design and analysis scheme that was designed specifically to consider multiple alternative hypotheses regarding factors that dictate local species’ densities. We anticipated persistent effects of hypoxia (felt even when seasonal hypoxia was absent) on species densities would be most pronounced for sessile species, but that immediate effects (felt only when seasonal hypoxia was present) would dominate for mobile species. Conventional analysis provided strong evidence that densities of sessile species were persistently reduced in the hypoxic-impacted site, but did not indicate widespread immediate density responses during hypoxic events among mobile species. The absence of strong weights of evidence for hypoxia effects was partly a consequence of alternative hypotheses that better explained spatial-temporal variation in species’ densities. The hierarchical ensemble analysis improved the precision of species-specific effect sizes, and also allowed us to make inferences about the response of aggregated groups of species. The estimated mean density reductions during hypoxic events (dissolved oxygen ~2 mg/l) ranged from 73 to 98% among mobile invertebrates, benthic, and benthopelagic fishes. The large reduction in benthic and benthopelagic species suggests substantial effects of hypoxia in Hood Canal even at oxygen levels that were marginally hypoxic. Understanding the full ecological consequence of hypoxia will require a greater knowledge on the spatial extent of distributional shifts and their effects on competitive and predator–prey interactions.     

Constant Hazard Function Models with a Change Point: A Bayesian Analysis Using Markov Chain Monte Carlo Methods

Metropolis algorithms along with Gibbs steps are proposed to perform a Bayesian analysis for change-point constant hazard function models considering different prior densities for the parameters and censored survival data. We also present some generalizations for the comparison of two treatments. The methodology is illustrated with some examples.     

Predictors for Physical Activity in Adolescent Girls Using Statistical Shrinkage Techniques for Hierarchical Longitudinal Mixed Effects Models

We examined associations among longitudinal, multilevel variables and girls’ physical activity to determine the important predictors for physical activity change at different adolescent ages. The Trial of Activity for Adolescent Girls 2 study (Maryland) contributed participants from 8th (2009) to 11th grade (2011) (n=561). Questionnaires were used to obtain demographic, and psychosocial information (individual- and social-level variables); height, weight, and triceps skinfold to assess body composition; interviews and surveys for school-level data; and self-report for neighborhood-level variables. Moderate to vigorous physical activity minutes were assessed from accelerometers. A doubly regularized linear mixed effects model was used for the longitudinal multilevel data to identify the most important covariates for physical activity. Three fixed effects at the individual level and one random effect at the school level were chosen from an initial total of 66 variables, consisting of 47 fixed effects and 19 random effects variables, in additional to the time effect. Self-management strategies, perceived barriers, and social support from friends were the three selected fixed effects, and whether intramural or interscholastic programs were offered in middle school was the selected random effect. Psychosocial factors and friend support, plus a school’s physical activity environment, affect adolescent girl’s moderate to vigorous physical activity longitudinally.     

Fungicide Effects on Fungal Community Composition in the Wheat Phyllosphere

The fungicides used to control diseases in cereal production can have adverse effects on non-target fungi, with possible consequences for plant health and productivity. This study examined fungicide effects on fungal communities on winter wheat leaves in two areas of Sweden. High-throughput 454 sequencing of the fungal ITS2 region yielded 235 operational taxonomic units (OTUs) at the species level from the 18 fields studied. It was found that commonly used fungicides had moderate but significant effect on fungal community composition in the wheat phyllosphere. The relative abundance of several saprotrophs was altered by fungicide use, while the effect on common wheat pathogens was mixed. The fungal community on wheat leaves consisted mainly of basidiomycete yeasts, saprotrophic ascomycetes and plant pathogens. A core set of six fungal OTUs representing saprotrophic species was identified. These were present across all fields, although overall the difference in OTU richness was large between the two areas studied.     

Does Mortality Vary between Asian Subgroups in New Zealand: An Application of Hierarchical Bayesian Modelling

The aim of this paper was to see whether all-cause and cause-specific mortality rates vary between Asian ethnic subgroups, and whether overseas born Asian subgroup mortality rate ratios varied by nativity and duration of residence. We used hierarchical Bayesian methods to allow for sparse data in the analysis of linked census-mortality data for 25–75 year old New Zealanders. We found directly standardised posterior all-cause and cardiovascular mortality rates were highest for the Indian ethnic group, significantly so when compared with those of Chinese ethnicity. In contrast, cancer mortality rates were lowest for ethnic Indians. Asian overseas born subgroups have about 70% of the mortality rate of their New Zealand born Asian counterparts, a result that showed little variation by Asian subgroup or cause of death. Within the overseas born population, all-cause mortality rates for migrants living 0–9 years in New Zealand were about 60% of the mortality rate of those living more than 25 years in New Zealand regardless of ethnicity. The corresponding figure for cardiovascular mortality rates was 50%. However, while Chinese cancer mortality rates increased with duration of residence, Indian and Other Asian cancer mortality rates did not. Future research on the mechanisms of worsening of health with increased time spent in the host country is required to improve the understanding of the process, and would assist the policy-makers and health planners.     

Community Involvement in Dengue Outbreak Control: An Integrated Rigorous Intervention Strategy

BackgroundAn explosive outbreak of dengue fever occurred in Guangdong Province, China in 2014. A community-based integrated intervention was applied to control this outbreak in the capital city Guangzhou, where dengue epidemic was mainly caused by imported cases.ConclusionsThis study suggests that an integrated dengue intervention program has significant effects to control a dengue outbreak in areas where dengue epidemic was mainly caused by imported dengue cases.     

Estimation of Coast-Wide Population Trends of Marbled Murrelets in Canada Using a Bayesian Hierarchical Model

Species at risk with secretive breeding behaviours, low densities, and wide geographic range pose a significant challenge to conservation actions because population trends are difficult to detect. Such is the case with the Marbled Murrelet (Brachyramphus marmoratus), a seabird listed as ‘Threatened’ by the Species at Risk Act in Canada largely due to the loss of its old growth forest nesting habitat. We report the first estimates of population trend of Marbled Murrelets in Canada derived from a monitoring program that uses marine radar to detect birds as they enter forest watersheds during 923 dawn surveys at 58 radar monitoring stations within the six Marbled Murrelet Conservation Regions on coastal British Columbia, Canada, 1996–2013. Temporal trends in radar counts were analyzed with a hierarchical Bayesian multivariate modeling approach that controlled for variation in tilt of the radar unit and day of year, included year-specific deviations from the overall trend (‘year effects’), and allowed for trends to be estimated at three spatial scales. A negative overall trend of -1.6%/yr (95% credibility interval: -3.2%, 0.01%) indicated moderate evidence for a coast-wide decline, although trends varied strongly among the six conservation regions. Negative annual trends were detected in East Vancouver Island (-9%/yr) and South Mainland Coast (-3%/yr) Conservation Regions. Over a quarter of the year effects were significantly different from zero, and the estimated standard deviation in common-shared year effects between sites within each region was about 50% per year. This large common-shared interannual variation in counts may have been caused by regional movements of birds related to changes in marine conditions that affect the availability of prey.     

Impact of NGO Training and Support Intervention on Diarrhoea Management Practices in a Rural Community of Bangladesh: An Uncontrolled,Single-Arm Trial

Purpose/Objective

The evolving Non-Governmental Organization (NGO) sector in Bangladesh provides health services directly, however some NGOs indirectly provide services by working with unlicensed providers. The primary objective of this study was to examine the impact of NGO training of unlicensed providers on diarrhoea management and the scale up of zinc treatment in rural populations.

Methods

An uncontrolled, single-arm trial for a training and support intervention on diarrhoea outcomes was employed in a rural sub-district of Bangladesh during 2008. Two local NGOs and their catchment populations were chosen for the study. The intervention included training of unlicensed health care providers in the management of acute childhood diarrhoea, particularly emphasizing zinc treatment. In addition, community-based promotion of zinc treatment was carried out. Baseline and endline ecologic surveys were carried out in intervention and control villages to document changes in treatments received for diarrhoea in under-five children.

Results

Among surveyed household with an active or recent acute childhood diarrhoea episode, 69% sought help from a health provider. Among these, 62.8% visited an unlicensed private provider. At baseline, 23.9% vs. 22% of control and intervention group children with diarrhoea had received zinc of any type. At endline (6 months later) this had changed to 15.3% vs. 30.2%, respectively. The change in zinc coverage was significantly higher in the intervention villages (p<0.01). Adherence with giving zinc for 10 days or more was significantly higher in the intervention households (9.2% vs. 2.5%; p<0.01). Child''s age, duration of diarrhoea, type of diarrhoea, parental year of schooling as well as oral rehydration solution (ORS) and antibiotic usage were significant predictors of zinc usage.

Conclusion

Training of unlicensed healthcare providers through NGOs increased zinc coverage in the diarrhoea management of under-five children in rural Bangladesh households.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT02143921","term_id":"NCT02143921"}}NCT02143921