Bayesian model evidence as a practical alternative to deviance information criterion

Delineating maternal influence in regulation of variance in major economic traits of White Leghorns: Bayesian insights

Proper variance partitioning and estimation of genetic parameters at appropriate time interval is crucial for understanding the dynamics of trait variance and genetic correlations and for deciding the future breeding strategy of the population. This study was conducted on the same premise to estimate genetic parameters of major economic traits in a White Leghorn strain IWH using Bayesian approach and to identify the role of maternal effects in the regulation of trait variance. Three different models incorporating the direct additive effect (Model 1), direct additive and maternal genetic effect (Model 2) and direct additive, maternal genetic and maternal permanent environmental effects (Model 3) were tried to estimate the genetic parameters for body weight traits (birth weight, body weight at 16, 20, 40 and 52 weeks), Age at sexual maturity (ASM), egg production traits (egg production up to 24, 28, 40, 52, 64 and 72 weeks) and egg weight traits (egg weight at 28, 40 and 52 weeks). Model 2 and Model 3 with maternal effects were found to be the best having the highest accuracy for almost all the traits. The direct additive genetic heritability was moderate for ASM, moderate to high for body weight traits and egg weight traits and low to moderate for egg production traits. Though the maternal heritability (h2mat) and permanent environmental effect (c2mpe) was low (<0.1) for most of the traits, they formed an important component of trait variance. Traits like egg weight at 28 weeks (0.14±0.06) and egg production at 72 weeks (0.13±0.07) reported comparatively higher values for c2mpe and h2mat respectively. Additive genetic correlation was high and positive between body weight traits, between egg weight traits, between consecutive egg production traits and between body weight and egg weight traits. However, a negative genetic correlation existed between egg production and egg weight traits, egg production and body weight traits, ASM and early egg production traits. Overall, a moderate positive genetic correlation was estimated between ASM and body weight traits and ASM and egg weight traits. Based on our findings, we can deduce that maternal effects constitute an important source of variation for all the major economic traits in White Leghorn and should be necessarily considered in genetic evaluation programs.

Introduction to Bayesian Analyses for Clinical Research

Bayesian analyses are becoming more popular as a means of analyzing data, yet the Bayesian approach is novel to many members of the broad clinical audience. While Bayesian analyses are foundational to anesthesia pharmacokinetic/pharmacodynamic modeling, they also can be used for analyzing data from clinical trials or observational studies. The traditional null hypothesis significance testing (frequentist) approach uses only the data collected from the current study to make inferences. On the other hand, the Bayesian approach quantifies the external information or expert knowledge and combines the external information with the study data, then makes inference from this combined information. We introduce to the clinical and translational science researcher what it means to do Bayesian statistics, why a researcher would choose to perform their analyses using the Bayesian approach, when it would be advantageous to use a Bayesian instead of a frequentist approach, and how Bayesian analyses and interpretations differ from the more traditional frequentist methods. Throughout this paper, we use various pain- and anesthesia-related examples to highlight the ideas and statistical concepts that should be relatable to other areas of research as well.

Nested Sampling aided determination of tantalum optical constants in the EUV spectral range

We report on determining the optical constants of Ta in the sub-extreme ultraviolet (EUV) spectral range 5.0-24.0 nm from the angle-dependent reflectance (ADR) measured using monochromatized synchrotron radiation. Two sputtered samples with differing thicknesses were investigated. Complementarily x-ray reflectance was measured at shorter wavelengths and evaluated by Fourier transform to facilitate an unambiguous selection of a model for the data evaluation based on an inverse solution of the Fresnel's equations for a layered system. Bayesian inferences coupled with a Nested Sampling (NS) algorithm were utilized to derive the optical constants with their corresponding uncertainties. This report further emphasizes the applicability of an acclaimed NS algorithm on a high-dimensional inverse problem. We explore the possibility of addressing the correlations between the optical constants of thin films and their structural parameters based on other established studies.

Neural mass model-based study of frontal-temporal theta oscillations in human subjects during the performance of a cognitive control task

Cognitive control, the ability to rapidly shift one's attention and behavioral strategy in response to environmental changes, is often compromised across psychiatric disorders. One of the well-validated behavioral paradigms for tapping into the cognitive control circuits is a cognitive interference task, where subjects must suppress a natural response to follow a less intuitive rule. Slower response times on these tasks indicate difficulty exerting control to overcome response conflict. Conflict evokes robust electrophysiological signatures, such as theta (4-8 Hz) oscillations in the prefrontal cortex (PFC). However, the underlying neural mechanisms of conflict-evoked theta oscillations in the PFC are not clear. The objective of this work is to use a neural mass model (NMM) to find feasible cortical networks generating theta oscillations during conflict processing in human subjects. We used intracranial EEG (iEEG) recorded from dorsolateral PFC (dIPFC) and lateral temporal lobe (LTL) of human subjects with intractable epilepsy undergoing invasive monitoring, while they performed a multi-source interference task (MSIT). We used a dynamic causal modeling (DCM) framework to simulate dIPFC-LTL theta using a Jansen-Rit NMM. We found significant evidence for an LTL input into the dlPFC during the initial 500 ms of conflict processing compared to a bidirectional connection between the dlPFC and LTL. We conclude that a neural mass modeling framework can be used to elucidate candidate mechanisms of neural oscillations underlying conflict resolution in human subjects. Clinical Relevance- This can be used to find feasible target mechanisms for designing therapy in patients with compromised cognitive control. This framework can also be expanded to serve as an in-silico test bed for designing and testing neuromodulatory interventions such as electrical stimulation for improving cognitive control in mood/anxiety disorders.

When and why direct transmission models can be used for environmentally persistent pathogens

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen’s characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models’ mean outputs diverge, with distinct predictions of outbreak size and duration.

Mathematical models of the spread and progression of communicable disease in populations are important tools in efforts to prevent and control outbreaks. A common class of disease models assume that infection is transmitted directly from infectious to susceptible individuals when they are in close proximity—so called direct transmission models. These are used widely and have proven invaluable as simplified descriptions of a wide array of infectious diseases in diverse populations. However, many pathogens spread through indirect, environmental routes of transmission, for example via contact with contaminated water sources in the case of cholera, or inhalation of infectious airborne droplets for respiratory infections, such as Covid-19.

We show that direct transmission models work well for such pathogens with short environmental lifetimes and where hosts shed pathogens into the environment at high rates. This means that we do not require information about environmental pathogen levels to understand the behaviour of outbreaks caused by these pathogens. When shedding rates are also low, e.g., with macroparasitic infections, or when variable environmental factors play a role in transmissibility, then explicit modelling of both the pathogen and environmental transmission will provide a more accurate picture than a direct transmission approximation.

Comparative Efficacy and Safety of Dopamine Agonists in Advanced Parkinson's Disease With Motor Fluctuations: A Systematic Review and Network Meta-Analysis of Double-Blind Randomized Controlled Trials

Background: Movement fluctuations are the main complication of Parkinson's disease (PD) patients receiving long-term levodopa (L-dopa) treatment. We compared and ranked the efficacy and safety of dopamine agonists (DAs) with regard to motor fluctuations by using a Bayesian network meta-analysis (NMA) to quantify information from randomized controlled trials (RCTs).

Methods and Findings: We carried out a systematic review and meta-analysis, and only RCTs comparing DAs for advanced PD were included. Electronic databases (PubMed, Embase, and Cochrane Library) were systematically searched for relevant studies published until January 2021. Two reviewers independently extracted individual study data and evaluated studies for risk of bias using the Cochrane Risk of Bias tool. Network meta-analyses using a Bayesian framework were used to calculate the related parameters. The pre-specified primary and secondary outcomes were efficacy (“ON” time without troublesome dyskinesia, “OFF” time, “ON” time, “UPDRS-III,” and “UPDRS-II”) and safety [treatment-emergent adverse events (TEAE) and other adverse events] of DAs. The results are presented as the surface under the cumulative ranking (SUCRA) curve. A total of 20 RCTs assessing 6,560 patients were included. The general DA effects were ranked from high to low with respect to the amount of “ON” time without troublesome dyskinesia as follows: apomorphine (SUCRA = 97.08%), pramipexole_IR (probability = 79.00%), and ropinirole_PR (SUCRA = 63.92%). The general safety of DAs was ranked from high to low with respect to TEAE as follows: placebo (SUCRA = 74.49%), pramipexole_ER (SUCRA = 63.6%), sumanirole (SUCRA = 54.07%), and rotigotine (SUCRA = 53.84%).

Conclusions: This network meta-analysis shows that apomorphine increased “ON” time without troublesome dyskinesia and decreased “OF” time for advanced PD patients. The addition of pramipexole, ropinirole, or rotigotine to levodopa treatment in advanced PD patients with motor fluctuations increased “ON” time without troublesome dyskinesia, improved the UPDRS III scores, and ultimately ameliorated the UPDRS II scores, thereby maximizing its benefit. This NMA of pramipexole, ropinirole, and rotigotine represents an effective treatment option and has an acceptable safety profile in patients with advanced PD.

Epidemiology and Regional Predictors of COVID-19 Clusters: A Bayesian Spatial Analysis Through a Nationwide Contact Tracing Data

Purpose: Revealing the clustering risks of COVID-19 and prediction is essential for effective quarantine policies, since clusters can lead to rapid transmission and high mortality in a short period. This study aimed to present which regional and social characteristics make COVID-19 cluster with high risk. Methods: By analyzing the data of all confirmed cases (14,423) in Korea between January 10 and August 3, 2020, provided by the Korea Disease Control and Prevention Agency, we manually linked each case and discovered clusters. After classifying the cases into clusters as nine types, we compared the duration and size of clusters by types to reveal high-risk cluster types. Also, we estimated odds for the risk factors for COVID-19 clustering by a spatial autoregressive model using the Bayesian approach. Results: Regarding the classified clusters (n = 539), the mean size was 19.21, and the mean duration was 9.24 days. The number of clusters was high in medical facilities, workplaces, and nursing homes. However, multilevel marketing, religious facilities, and restaurants/business-related clusters tended to be larger and longer when an outbreak occurred. According to the spatial analysis in COVID-19 clusters of more than 20 cases, the global Moran's I statistics value was 0.14 (p < 0.01). After adjusting for population size, the risks of COVID-19 clusters were related to male gender (OR = 1.29) and low influenza vaccination rate (OR = 0.87). After the spatial modeling, the predicted probability of forming clusters was visualized and compared with the actual incidence and local Moran's I statistics 2 months after the study period. Conclusions: COVID-19 makes different sizes of clusters in various contact settings; thus, precise epidemic control measures are needed. Also, when detecting and screening for COVID-19 clusters, regional risks such as vaccination rate should be considered for predicting risk to control the pandemic cost-effectively.

Network Meta-Analysis of Different Clinical Commonly Used Drugs for the Treatment of Hypertrophic Scar and Keloid

Background: There is no uniform treatment for pathological scars, including keloids and hypertrophic scars, in clinic currently. Previously, multiple randomized controlled trials have examined the clinical efficacy of different treatments. Nonetheless, the results are inconsistent, and many treatments have not been directly compared. This makes it difficult to conclude which approach is more favorable, in terms of efficacy and safety, for the treatment of pathological scarring. This study aimed at evaluating the efficacy of different injection and topical treatment strategies for hypertrophic scar and keloid.

Methods: Relevant literature from PubMed, Medline, Embase, Scopus, the Cochrane Central Register of Controlled Trials (CCRCT), and WHO International Clinical Trials Registry Platform (WHO-ICTRP) were searched, from database inception through November 2020. Randomized clinical trials evaluating different treatment strategies of pathological scars, including triamcinolone acetonide (TAC), verapamil (VER), 5-fluorouracil (5-FU), botulinum toxin A (BTA), bleomycin (BLM), and silicone gels were included in the study.

Results: The network meta-analysis included a total of 2,009 patients from 29 studies. A network meta-analysis of injection and topical treatment strategies showed that the efficacy of TAC combined with BTA was best in the treatment of pathological scars. Combination therapies of TAC with 5-FU and TAC with BTA significantly improved the clinical efficiency. However, there was no statistically significant difference between other treatment strategies. The order of efficacy predicted by the surface under the cumulative ranking (SUCRA) curve was as follows: TAC+BTA (82.2%) > TAC+5-FU (69.8%) > BTA (67.3%) > 5-FU+silicone (59.4%) > TAC+silicone (58.3%) > 5-FU (49.8%) > BLM (42.0%) > TAC (26.7%) > VER (26.2%) > silicone (18.3%). There was no publication bias revealed based on the funnel diagram.

Conclusion: This study recommends intralesional injection of TAC-BTA and TAC-5-FU combined therapies. But for patients who cannot tolerate the side effects, the use of silicone gels in combination with TAC is recommended. However, these conclusions need to be further confirmed by more randomized controlled trials.

Development of influenza-associated disease burden pyramid in Shanghai, China, 2010-2017: a Bayesian modelling study

OBJECTIVES

Negative estimates can be produced when statistical modelling techniques are applied to estimate morbidity and mortality attributable to influenza. Based on the prior knowledge that influenza viruses are hazardous pathogens and have adverse health outcomes of respiratory and circulatory disease (R&C), we developed an improved model incorporating Bayes' theorem to estimate the disease burden of influenza in Shanghai, China, from 2010 to 2017.

DESIGN

A modelling study using aggregated data from administrative systems on weekly R&C mortality and hospitalisation, influenza surveillance and meteorological data. We constrained the regression coefficients for influenza activity to be positive by truncating the prior distributions at zero.

SETTING

Shanghai, China.

PARTICIPANTS

People registered with R&C deaths (450 298) and hospitalisations (2621 787, from 1 July 2013), and with influenza-like illness (ILI) outpatient visits (342 149) between 4 January 2010 and 31 December 2017.

PRIMARY OUTCOME MEASURES

Influenza-associated disease burden (mortality, hospitalisation and outpatient visit rates) and clinical severity (outpatient-mortality, outpatient-hospitalisation and hospitalisation-mortality risks).

RESULTS

Influenza was associated with an annual average of 15.49 (95% credibility interval (CrI) 9.06-22.06) excess R&C deaths, 100.65 (95% CrI 48.79-156.78) excess R&C hospitalisations and 914.95 (95% CrI 798.51-1023.66) excess ILI outpatient visits per 100 000 population in Shanghai. 97.23% and 80.24% excess R&C deaths and hospitalisations occurred in people aged ≥65 years. More than half of excess morbidity and mortality were associated with influenza A(H3N2) virus, and its severities were 1.65-fold to 3.54-fold and 1.47-fold to 2.16-fold higher than that for influenza A(H1N1) and B viruses, respectively.

CONCLUSIONS

The proposed Bayesian approach with reasonable prior information improved estimates of influenza-associated disease burden. Influenza A(H3N2) virus was generally associated with higher morbidity and mortality, and was relatively more severe compared with influenza A(H1N1) and B viruses. Targeted influenza prevention and control strategies for the elderly in Shanghai may substantially reduce the disease burden.

A Bayesian approach to model the trends and variability in urban stormwater quality associated with catchment and hydrologic parameters

Stormwater runoff pollution has become a key environmental issue in urban areas. Reliable estimation of stormwater pollutant discharge is important for implementing robust water quality management strategies. Even though significant attempts have been undertaken to develop water quality models, deterministic approaches have proven inappropriate as they do not address the variability in stormwater quality. Due to the random nature of rainfall characteristics and the differences in catchment characteristics, it is difficult to generate the runoff pollutographs to a desired level of certainty. Bayesian hierarchical modelling is an effective tool for developing complex models with a large number of sources of variability. A Bayesian model does not look for a single value of the model parameters, but rather determines a distribution of the model parameters from which all inference is drawn. This study introduces a Bayesian hierarchical linear regression model to describe a catchment specific runoff pollutograph incorporating the associated uncertainties in the model parameters. The model incorporates catchment and rainfall characteristics including the effective impervious area, time of concentration, rain duration, average rainfall intensity and the antecedent dry period as the contributors to random effects.

Estimation of the sensitivity and specificity of four serum ELISA and one fecal PCR for diagnosis of paratuberculosis in adult dairy cattle in New Zealand using Bayesian latent class analysis

In New Zealand, a new diagnostic approach for the control of paratuberculosis in mixed aged milking cows has been developed using a combination of ELISA and quantitative fecal PCR (f-qPCR). Our analysis was designed to evaluate performance of these individual tests in infected or infectious mixed aged cows across the prevalence of infection typically encountered on NZ dairy farms and calculate test accuracy when used as a screening test of serological ELISAs for four separate antigens read in parallel followed by a confirmatory quantitative f-qPCR test. Data from a cross-sectional study of 20 moderate prevalence herds was combined with existing data from 2 low and 20 high prevalence herds forming a dataset of 3845 paired serum and fecal samples. Incidence of clinical Johne's disease (JD) was used to classify herds into three prevalence categories. High (≥ 3% annual clinical JD for the last three years), moderate (<3 - 1%) and low (<1% incidence for at least the last five years). Positive tests were declared if> 50 ELISA units and f-qPCR at two cut-points (≥1 × 10⁴ genomes/mL or >1 × 10³ genomes/mL). Fixed Bayesian latent class models at both f-qPCR cut-points, accounted for conditional independence and paired conditional dependence. Mixed models at both f-qPCR cut-points, using a different mechanism to account for conditional dependencies between tests were also implemented. Models (24 in number) were constructed using OpenBUGS. The aim was to identify Mycobacterium avium subsp. paratuberculosis (MAP) infected cows that met at least one of two criteria: shedding sufficient MAP in feces to be detected by f-qPCR or mounting a detectable MAP antibody response. The best fit to the data was obtained by modelling pairwise dependencies between tests in a fixed model or by accounting for dependencies in a mixed model at a fecal cut-off of ≥1 × 10⁴ genomes/mL. Test performance differed with prevalence, but models were robust to prior assumptions. For the fixed model, at a prevalence of 0.29 (95 % probability interval (PI) = 0.25-0.33), as a screening plus confirmatory f-qPCR, post-test probability for disease in a positive animal was 0.84 (95 %PI = 0.80-0.88) and 0.16 (95 %PI = 0.15-0.18) for disease in a test negative animal. In low prevalence herds (0.01(95 %PI = 0.00-0.04)) the equivalent figures were 0.84 (95 %PI = 0.08-0.92) and 0.00 (95 %PI = 0.00-0.02). These results suggest this is a useful tool to control JD on dairy farms, particularly in herds with higher levels of infection, where the sampling and testing cost per animal is defrayed across more detected animals.

New insights about vaccine effectiveness: Impact of attenuated PRRS-strain vaccination on heterologous strain transmission

Vaccination is the main tool for controlling infectious diseases in livestock. Yet current vaccines only provide partial protection raising concerns about vaccine effectiveness in the field. Two successive transmission trials were performed involving 52 pigs to evaluate the effectiveness of a Porcine Reproductive and Respiratory Syndrome (PRRS) vaccinal strain candidate against horizontal transmission of a virulent heterologous strain. PRRS virus, above the specified limit of detection, was observed in serum and nasal secretions for all but one pig (the exception only tested positive for serum), indicating that vaccination did not protect pigs from becoming infected and shedding the heterologous strain. However, vaccination delayed the onset of viraemia, reduced the duration of shedding and significantly decreased viral load throughout infection. Serum antibody profiles indicated that 4 out of 13 (31%) vaccinates in one trial had no serological response (NSR). A Bayesian epidemiological model was fitted to the data to assess the impact of vaccination and presence of NSRs on PRRS virus transmission dynamics. Despite little evidence for reduction in the transmission rate, vaccinated animals were on average slower to become infectious, experienced a shorter infectious period and recovered faster. The overall PRRSV transmission potential, represented by the reproductive ratio R₀ was lower for the vaccinated animals, although there was substantial overlap in the credibility intervals for both groups. Model selection suggests that transmission parameters of vaccinated pigs with NSR were more similar to those of unvaccinated animals. The presence of NSRs in a population, however, seemed to only marginally affect the transmission dynamics. The results suggest that even when vaccination can't prevent infection, it can still have beneficial impacts on the transmission dynamics and contribute to reducing a herd's R₀. However, biosecurity and other measures need to be considered to decrease contact rates and lower R₀ below 1.

Bayesian model discrimination for partially-observed epidemic models

An efficient method for Bayesian model selection is presented for a broad class of continuous-time Markov chain models and is subsequently applied to two important problems in epidemiology. The first problem is to identify the shape of the infectious period distribution; the second problem is to determine whether individuals display symptoms before, at the same time, or after they become infectious. In both cases we show that the correct model can be identified, in the majority of cases, from symptom onset data generated from multiple outbreaks in small populations. The method works by evaluating the likelihood using a particle filter that incorporates a novel importance sampling algorithm designed for partially-observed continuous-time Markov chains. This is combined with another importance sampling method to unbiasedly estimate the model evidence. These come with estimates of precision, which allow for stopping criterion to be employed. Our method is general and can be applied to a wide range of model selection problems in biological and epidemiological systems with intractable likelihood functions.

Parental habituation to human disturbance over time reduces fear of humans in coyote offspring

A fundamental tenet of maternal effects assumes that maternal variance over time should have discordant consequences for offspring traits across litters. Yet, seldom are parents observed across multiple reproductive bouts, with few studies considering anthropogenic disturbances as an ecological driver of maternal effects. We observed captive coyote (Canis latrans) pairs over two successive litters to determine whether among‐litter differences in behavior (i.e., risk‐taking) and hormones (i.e., cortisol and testosterone) corresponded with parental plasticity in habituation. Thus, we explicitly test the hypothesis that accumulating experiences of anthropogenic disturbance reduces parental fear across reproductive bouts, which should have disparate phenotypic consequences for first‐ and second‐litter offspring. To quantify risk‐taking behavior, we used foraging assays from 5–15 weeks of age with a human observer present as a proxy for human disturbance. At 5, 10, and 15 weeks of age, we collected shaved hair to quantify pup hormone levels. We then used a quantitative genetic approach to estimate heritability, repeatability, and between‐trait correlations. We found that parents were riskier (i.e., foraged more frequently) with their second versus first litters, supporting our prediction that parents become increasingly habituated over time. Second‐litter pups were also less risk‐averse than their first‐litter siblings. Heritability for all traits did not differ from zero (0.001–0.018); however, we found moderate support for repeatability in all observed traits (r = 0.085–0.421). Lastly, we found evidence of positive phenotypic and cohort correlations among pup traits, implying that cohort identity (i.e., common environment) contributes to the development of phenotypic syndromes in coyote pups. Our results suggest that parental habituation may be an ecological cue for offspring to reduce their fear response, thus emphasizing the role of parental plasticity in shaping their pups’ behavioral and hormonal responses toward humans.