JAGS model specification for spatiotemporal epidemiological modelling.

Bayesian inference in modelling infectious diseases using Bayesian inference using Gibbs Sampling (BUGS) is notable in the last two decades in parallel with the advancements in computing and model development. The ability of BUGS to easily implement the Markov chain Monte Carlo (MCMC) method brought Bayesian analysis to the mainstream of infectious disease modelling. However, with the existing software that runs MCMC to make Bayesian inferences, it is challenging, especially in terms of computational complexity, when infectious disease models become more complex with spatial and temporal components, in addition to the increasing number of parameters and large datasets. This study investigates two alternative subscripting strategies for creating models in Just Another Gibbs Sampler (JAGS) environment and their performance in terms of run times. Our results are useful for practitioners to ensure the efficiency and timely implementation of Bayesian spatiotemporal infectious disease modelling.

Assessing the impact of Australia's mass vaccination campaigns over the Delta and Omicron outbreaks.

BACKGROUND: The Australian Government implemented a national vaccination campaign against COVID-19 beginning February 22, 2021. The roll-out was criticised for being delayed relative to many high-income countries, but high levels of vaccination coverage were belatedly achieved. The large-scale Omicron outbreak in January 2022 resulted in a massive number of cases and deaths, although mortality would have been far higher if not for vigorous efforts to rapidly vaccinate the entire population. The impact of the vaccination coverage was assessed over this extended period. METHODS: We considered NSW, as the Australian jurisdiction with the highest quality data for our purposes and which still reflected the national experience. Weekly death rates were derived among individuals aged 50+ with respect to vaccine status between August 8, 2021 and July 9, 2022. We evaluated deaths averted by the vaccination campaign by modelling alternative counterfactual scenarios based on a simple data-driven modelling methodology presented by Jia et al. (2023). FINDINGS: Unvaccinated individuals had a 7.7-fold greater mortality rate than those who were fully vaccinated among people aged 50+, which rose to 11.2-fold in those who had received a booster dose. If NSW had fully vaccinated its ~2.9 million 50+ residents earlier (by July 28, 2021), only 440 of the total 3,495 observed 50+ deaths would have been averted. Up to July 9, 2022, the booster campaign prevented 1,860 deaths. In the absence of a vaccination campaign, ~21,250 COVID-19 50+ deaths (conservative estimate) could have been expected in NSW i.e., some 6 times the actual total. We also find the methodology of Jia et al. (2023) can sometimes significantly underestimate that actual number. INTERPRETATION: The Australian vaccination campaign was successful in reducing mortality over 2022, relative to alternative hypothetical vaccination scenarios. The success was attributable to the Australian public's high levels of engagement with vaccination in the face of new SARS-COV-2 variants, and because high levels of vaccination coverage (full and booster) were achieved in the period shortly before the major Omicron outbreak of 2022.

Resolving the enigma of Iquitos and Manaus: A modeling analysis of multiple COVID-19 epidemic waves in two Amazonian cities.

The two nearby Amazonian cities of Iquitos and Manaus endured explosive COVID-19 epidemics and may well have suffered the world's highest infection and death rates over 2020, the first year of the pandemic. State-of-the-art epidemiological and modeling studies estimated that the populations of both cities came close to attaining herd immunity (>70% infected) at the termination of the first wave and were thus protected. This makes it difficult to explain the more deadly second wave of COVID-19 that struck again in Manaus just months later, simultaneous with the appearance of a new P.1 variant of concern, creating a catastrophe for the unprepared population. It was suggested that the second wave was driven by reinfections, but the episode has become controversial and an enigma in the history of the pandemic. We present a data-driven model of epidemic dynamics in Iquitos, which we also use to explain and model events in Manaus. By reverse engineering the multiple epidemic waves over 2 y in these two cities, the partially observed Markov process model inferred that the first wave left Manaus with a highly susceptible and vulnerable population (≈40% infected) open to invasion by P.1, in contrast to Iquitos (≈72% infected). The model reconstructed the full epidemic outbreak dynamics from mortality data by fitting a flexible time-varying reproductive number [Formula: see text] while estimating reinfection and impulsive immune evasion. The approach is currently highly relevant given the lack of tools available to assess these factors as new SARS-CoV-2 virus variants appear with different degrees of immune evasion.

Detection of grey zones in inter-rater agreement studies.

BACKGROUND: In inter-rater agreement studies, the assessment behaviour of raters can be influenced by their experience, training levels, the degree of willingness to take risks, and the availability of clear guidelines for the assessment. When the assessment behaviour of raters differentiates for some levels of an ordinal classification, a grey zone occurs between the corresponding adjacent cells to these levels around the main diagonal of the table. A grey zone introduces a negative bias to the estimate of the agreement level between the raters. In that sense, it is crucial to detect the existence of a grey zone in an agreement table. METHODS: In this study, a framework composed of a metric and the corresponding threshold is developed to identify grey zones in an agreement table. The symmetry model and Cohen's kappa are used to define the metric, and the threshold is based on a nonlinear regression model. A numerical study is conducted to assess the accuracy of the developed framework. Real data examples are provided to illustrate the use of the metric and the impact of identifying a grey zone. RESULTS: The sensitivity and specificity of the proposed framework are shown to be very high under moderate, substantial, and near-perfect agreement levels for [Formula: see text] and [Formula: see text] tables and sample sizes greater than or equal to 100 and 50, respectively. Real data examples demonstrate that when a grey zone is detected in the table, it is possible to report a notably higher level of agreement in the studies. CONCLUSIONS: The accuracy of the proposed framework is sufficiently high; hence, it provides practitioners with a precise way to detect the grey zones in agreement tables.

Spatiotemporal Bayesian estimation of the number of under-reported COVID-19 cases in Victoria Australia.

Having an estimate of the number of under-reported cases is crucial in determining the true burden of a disease. In the COVID-19 pandemic, there is a great need to quantify the true disease burden by capturing the true incidence rate to establish appropriate measures and strategies to combat the disease. This study investigates the under-reporting of COVID-19 cases in Victoria, Australia, during the third wave of the pandemic as a result of variation in geographic area and time. It is aimed to determine potential under-reported areas and generate the true picture of the disease in terms of the number of cases. A two-tiered Bayesian hierarchical model approach is employed to estimate the true incidence and detection rates through Bayesian model averaging. The proposed model goes beyond testing inequality across areas by looking into other covariates such as weather, vaccination rates, and access to vaccination and testing centres, including interactions and variations between space and time. This model aims for parsimony yet allows a broader range of scope to capture the underlying dynamic of the reported COVID-19 cases. Moreover, it is a data-driven, flexible, and generalisable model to a global context such as cross-country estimation and across time points under strict pandemic conditions.

Bayesian estimation of the effect of health inequality in disease detection.

BACKGROUND: Measuring health inequality is essential to ensure that everyone has equal accessibility to health care. Studies in the past have continuously presented and showed areas or groups of people affected by various inequality in accessing the health resources and services to help improve this matter. Alongside, disease prevention is as important to minimise the disease burden and improve health and quality of life. These aspects are interlinked and greatly contributes to one's health. METHOD: In this study, the Gini coefficient and Lorenz curve are used to give an indication of the overall health inequality. The impact of this inequality in granular level is demonstrated using Bayesian estimation for disease detection. The Bayesian estimation used a two-component modelling approach that separates the case detection process and incidence rate using a mixed Poisson distribution while capturing underlying spatio-temporal characteristics. Bayesian model averaging is used in conjunction with the two-component modelling approach to improve the accuracy of estimates by incorporating many candidate models into the analysis instead of using fixed component models. This method is applied to an infectious disease, influenza, in Victoria, Australia between 2013 and 2016 and the corresponding primary health care of the state. RESULT: There is a relatively equal distribution of health resources and services pertaining to general practitioners (GP) and GP clinics in Victoria, Australia. Roughly 80 percent of the population shares 70 percent of the number of GPs and GP clinics. The Bayesian estimation with model averaging revealed that access difficulty to health services impacts both case detection probability and incidence rate. Minimal differences are recorded in the observed and estimated incidence of influenza cases considering social deprivation factors. In most years, areas in Victoria's southwest and eastern parts have potential under-reported cases consistent with their relatively lower number of GP or GP clinics. CONCLUSION: The Bayesian model estimated a slight discrepancy between the estimated incidence and the observed cases of influenza in Victoria, Australia in 2013-2016 period. This is consistent with the relatively equal health resources and services in the state. This finding is beneficial in determining areas with potential under-reported cases and under-served health care. The proposed approach in this study provides insight into the impact of health inequality in disease detection without requiring costly and time-extensive surveys and relying mainly on the data at hand. Furthermore, the application of Bayesian model averaging provided a flexible modelling framework that allows covariates to move between case detection and incidence models.

Bayesian prediction of emergency department wait time.

Increasingly, many hospitals are attempting to provide more accurate information about Emergency Department (ED) wait time to their patients. Estimation of ED wait time usually depends on what is known about the patient and also the status of the ED at the time of presentation. We provide a model for estimating ED wait time for prospective low acuity patients accessing information online prior to arrival. Little is known about the prospective patient and their condition. We develop a Bayesian quantile regression approach to provide an estimated wait time range for prospective patients. Our proposed approach incorporates a priori information in government statistics and elicited expert opinion. This methodology is compared to frequentist quantile regression and Bayesian quantile regression with non-informative priors. The test set includes 1, 024 low acuity presentations, of which 457 (44%) are Category 3, 425 (41%) are Category 4 and 160 (15%) are Category 5. On the Huber loss metric, the proposed method performs best on the test data for both median and 90th percentile prediction compared to non-informative Bayesian quantile regression and frequentist quantile regression. We obtain a benefit in the estimation of model coefficients due to the value contributed by a priori information in the form of elicited expert guesses guided by government wait time statistics. The use of such informative priors offers a beneficial approach to ED wait time prediction with demonstrable potential to improve wait time quantile estimates.

Bayesian approaches to the weighted kappa-like inter-rater agreement measures.

Inter-rater agreement measures are used to estimate the degree of agreement between two or more assessors. When the agreement table is ordinal, different weight functions that incorporate row and column scores are used along with the agreement measures. The selection of row and column scores is effectual on the estimated degree of agreement. The weighted measures are prone to the anomalies frequently seen in agreement tables such as unbalanced table structures or grey zones due to the assessment behaviour of the raters. In this study, Bayesian approaches for the estimation of inter-rater agreement measures are proposed. The Bayesian approaches make it possible to include prior information on the assessment behaviour of the raters in the analysis and impose order restrictions on the row and column scores. In this way, we improve the accuracy of the agreement measures and mitigate the impact of the anomalies in the estimation of the strength of agreement between the raters. The elicitation of prior distributions is described theoretically and practically for the Bayesian estimation of five agreement measures with three different weights using an agreement table having two grey zones. A Monte Carlo simulation study is conducted to assess the classification accuracy of the Bayesian and classical approaches for the considered agreement measures for a given level of agreement. Recommendations for the selection of the highest performing agreement measure and weight combination are made in the breakdown of the table structure and sample size.

The impact of grey zones on the accuracy of agreement measures for ordinal tables.

BACKGROUND: In an inter-rater agreement study, if two raters tend to rate considering different aspects of the subject of interest or have different experience levels, a grey zone occurs among the levels of a square contingency table showing the inter-rater agreement. These grey zones distort the degree of agreement between raters and negatively impact the decisions based on the inter-rater agreement tables. In this sense, it is important to know how the existence of a grey zone impacts the inter-rater agreement coefficients to choose the most reliable agreement coefficient against the grey zones to reach out with more reliable decisions. METHODS: In this article, we propose two approaches to create grey zones in simulations setting and conduct an extensive Monte Carlo simulation study to figure out the impact of having grey zones on the weighted inter-rater agreement measures for ordinal tables over a comprehensive simulation space. RESULTS: The weighted inter-rater agreement coefficients are not reliable against the existence of grey zones. Increasing sample size and the number of categories in the agreement table decreases the accuracy of weighted inter-rater agreement measures when there is a grey zone. When the degree of agreement between the raters is high, the agreement measures are not significantly impacted by the existence of grey zones. However, if there is a medium to low degree of inter-rater agreement, all the weighted coefficients are more or less impacted. CONCLUSIONS: It is observed in this study that the existence of grey zones has a significant negative impact on the accuracy of agreement measures especially for a low degree of true agreement and high sample and tables sizes. In general, Gwet's AC2 and Brennan-Prediger's κ with quadratic or ordinal weights are reliable against the grey zones.

Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates.

Transmission network modelling to infer 'who infected whom' in infectious disease outbreaks is a highly active area of research. Outbreaks of foot-and-mouth disease have been a key focus of transmission network models that integrate genomic and epidemiological data. The aim of this study was to extend Lau's systematic Bayesian inference framework to incorporate additional parameters representing predominant species and numbers of animals held on a farm. Lau's Bayesian Markov chain Monte Carlo algorithm was reformulated, verified and pseudo-validated on 100 simulated outbreaks populated with demographic data Japan and Australia. The modified model was then implemented on genomic and epidemiological data from the 2010 outbreak of foot-and-mouth disease in Japan, and outputs compared to those from the SCOTTI model implemented in BEAST2. The modified model achieved improvements in overall accuracy when tested on the simulated outbreaks. When implemented on the actual outbreak data from Japan, infected farms that held predominantly pigs were estimated to have five times the transmissibility of infected cattle farms and be 49% less susceptible. The farm-level incubation period was 1 day shorter than the latent period, the timing of the seeding of the outbreak in Japan was inferred, as were key linkages between clusters and features of farms involved in widespread dissemination of this outbreak. To improve accessibility the modified model has been implemented as the R package 'BORIS' for use in future outbreaks.

Impact of increasing temperature anomalies and carbon dioxide emissions on wheat production.

Climate change is one of the serious issues humankind is currently facing. It impacts almost all the processes in nature and threatens the existence of species and biodiversity; hence, the whole process of the food cycle. To mitigate the influence of climate change on vital processes in nature, we need to understand the pattern and magnitude of the relationship between climate change and impacted processes in nature. In this article, we explore the impact of climate change on wheat production in terms of short and long-run relationships between world wheat production, carbon dioxide emissions, and surface temperature anomalies. We present new information on the nexus between climate change and wheat production using autoregressive distributed lag (ARDL) models and ARDL bounds test of cointegration. We observe a significant cointegration relationship among world wheat production, carbon dioxide emissions, and surface temperature anomalies series. Lagged short-run impacts of temperature anomalies and carbon dioxide emissions are found significant. The long-run impact of both series on world wheat production is significant with a high correction speed to any instability between wheat production and the proxies of climate change.

dLagM: An R package for distributed lag models and ARDL bounds testing.

In this article, we introduce the R package dLagM for the implementation of distributed lag models and autoregressive distributed lag (ARDL) bounds testing to explore the short and long-run relationships between dependent and independent time series. Distributed lag models constitute a large class of time series regression models including the ARDL models used for cointegration analysis. The dLagM package provides a user-friendly and flexible environment for the implementation of the finite linear, polynomial, Koyck, and ARDL models and ARDL bounds cointegration test. Particularly, in this article, a new search algorithm to specify the orders of ARDL bounds testing is proposed and implemented by the dLagM package. Main features and input/output structures of the dLagM package and use of the proposed algorithm are illustrated over the datasets included in the package. Features of dLagM package are benchmarked with some mainstream software used to implement distributed lag models and ARDLs.

Incorporation of expert knowledge in the statistical detection of diagnosis related group misclassification.

BACKGROUND: In activity based funding systems, the misclassification of inpatient episode Diagnostic Related Groups (DRGs) can have significant impacts on the revenue of health care providers. Weakly informative Bayesian models can be used to estimate an episode's probability of DRG misclassification. METHODS: This study proposes a new, Hybrid prior approach which utilises guesses that are elicited from a clinical coding auditor, switching to non-informative priors where this information is inadequate. This model's ability to detect DRG revision is compared to benchmark weakly informative Bayesian models and maximum likelihood estimates. RESULTS: Based on repeated 5-fold cross-validation, classification performance was greatest for the Hybrid prior model, which achieved best classification accuracy in 14 out of 20 trials, significantly outperforming benchmark models. CONCLUSIONS: The incorporation of elicited expert guesses via a Hybrid prior produced a significant improvement in DRG error detection; hence, it has the ability to enhance the efficiency of clinical coding audits when put into practice at a health care provider.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

Inferring about the extinction of a species using certain and uncertain sightings.

The sighting record of threatened species is often used to infer the possibility of extinction. Most of these sightings have uncertain validity. Solow and Beet(2014) developed two models using a Bayesian approach which allowed for uncertainty in the sighting record by formally incorporating both certain and uncertain sightings, but in different ways. Interestingly, the two methods give completely different conclusions concerning the extinction of the Ivory-billed Woodpecker. We further examined these two methods to provide a mathematical explanation, and to explore in more depth, as to why the results differed from one another. It was found that the first model was more sensitive to the last uncertain sighting, while the second was more sensitive to the last certain sighting. The difficulties in choosing the appropriate model are discussed.

A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements.

Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors.

On an extended interpretation of linkage disequilibrium in genetic case-control association studies.

We are concerned with statistical inference for 2 × C × K contingency tables in the context of genetic case-control association studies. Multivariate methods based on asymptotic Gaussianity of vectors of test statistics require information about the asymptotic correlation structure among these test statistics under the global null hypothesis. In the case of C=2, we show that for a wide variety of test statistics this asymptotic correlation structure is given by the standardized linkage disequilibrium matrix of the K loci under investigation. Three popular choices of test statistics are discussed for illustration. In the case of C=3, the standardized composite linkage disequilibrium matrix is the limiting correlation matrix of the K locus-specific Cochran-Armitage trend test statistics.

Power comparisons for group sequential tests with nonparametric statistics in case of nonproportional hazards.

In clinical trials, it is important to set up a design to reach a decision on effectiveness of a drug in treating a disease with the loss of the minimum number of patients. Group sequential designs are very beneficial on this point. However, the proportional hazards assumption must hold to work under a group sequential design properly. A trial running under a group sequential design covers a long time period; therefore, assuming that hazards remain proportional over a long time period is somewhat unrealistic. We should examine and figure out the impact of nonproportional hazards over the hypothesis tests conducted under a group sequential design to set up more reliable designs and decide which test to use in which conditions. In this article, powers of group sequential tests with nonparametric statistics are evaluated under nonproportional hazards by a Monte Carlo simulation study. The simulation study covers different nonproportional hazards scenarios, censoring rates, survival distributions, sample sizes, and tied observations. With this study, we intend to be helpful for clinical trial designers to set up a more reliable group sequential design.

Quality of life among Turkish immigrants in Sweden.

OBJECTIVES: To assess quality of life among Turkish immigrants in Sweden by using the WHOQOL-100 scale and to evaluate the domains' contribution to explain the variance in the quality of life of the immigrants. Our hypothesis was QOL among Turkish immigrants in Sweden are better than Turkish people who are living in their home country. MATERIAL AND METHODS: This study was performed in the districts of Stockholm where Turkish immigrants have mostly settled. With the help and guidance of the Turkish Association, a sample of 520 participants was selected. We collected the demographic data by printed questionnaires, and to measure the quality of life, we used the WHOQOL-100 scale Turkish version. For analysis, we used the SPSS V.13.0 and R package programs, variance analyses, and Bayesian regression. RESULTS: The quality of life among the sample of Turkish immigrants was found to be moderate, but higher than the sample of the Turkish population. The quality of life of male immigrants was found to be higher than for females. Swedish-born Turks had better quality of life perceptions. CONCLUSION: Turkish immigrants' quality of life perceptions were better than those of the Turkish sample. The best scores were received from the third generation. The first generation and female immigrants need attention in order to receive higher quality of life perceptions.