Comparison of Different Count Models for Investigation of Some Environmental Factors Affecting Stillbirth in Holsteins

Background: The objective of this study is comparing different count data models for stillbirth data. In modeling this type of data, Poisson regression or alternative models can be preferred. Methods: The poisson, negative binomial, zero-inflated poisson, zero-inflated negative binomial, poisson-logit hurdle and negative binomial-logit hurdle regressions were compared and used to examine the effects of the gender, parity and herd-year-season independent variables on stillbirth. Furthermore, the Log-Likelihood statistics, Akaike Information Criteria, Bayesian Information Criteria and rootogram graphs were used as comparison criteria for performance of the models. According to these criteria, Negative Binomial-Logit Hurdle Regression model was chosen as the best model. Result: The parameter estimates obtained by Negative Binomial-Logit Hurdle Regression model in relation to the effects of the gender, parity and herd-year-season independent variables on stillbirth were found to be significant (p less than 0.01). It was found that while stillbirth incidence was higher in males than females, it was found to decrease as the parity increased. As a result, the Negative Binomial Logit Hurdle model was found the best model for stillbirth count data with overdispersion.

Fitting Empirical Fundamental Diagrams of Road Traffic: A Comprehensive Review and Comparison of Models Using an Extensive Data Set

<div>Understanding the inter-relationships between traffic flow, density, and speed through the study of the fundamental diagram of road traffic is critical for traffic modelling and management. Consequently, over the last 85 years, a wealth of models have been developed for its functional form. However, there has been no clear answer as to which model is the most appropriate for observed (i.e. empirical) fundamental diagrams and under which conditions. A lack of data has been partly to blame. Motivated by shortcomings in previous reviews, we first present a comprehensive literature review on modelling the functional form of empirical fundamental diagrams. We then perform fits of 50 previously proposed models to a high quality sample of 10,150 empirical fundamental diagrams pertaining to 25 cities. Comparing the fits using information criteria, we find that the non-parametric Sun model greatly outperforms all of the other models. The Sun model maintains its winning position regardless of road type and congestion level. Our study, the first of its kind when considering the number of models tested and the amount of data used, finally provides a definitive answer to the question ``Which model for the functional form of an empirical fundamental diagram is currently the best?''. The word ``currently'' in this question is key, because previously proposed models adopt an inappropriate Gaussian noise model with constant variance. We advocate that future research should shift focus to exploring more sophisticated noise models. This will lead to an improved understanding of empirical fundamental diagrams and their underlying functional forms.</div><div><br></div><div>Accepted by IEEE Transactions On Intelligent Transportation Systems on 14th Dec 2021<br></div><br>

A Family of Bayesian Estimators for the Two-Parametric Burr Type II Distribution

This study discusses the posterior estimation for the parameters of the Burr type II distribution (BIID). The informative and noninformative priors along with different loss functions have also been assumed for the posterior estimation. The applicability of the proposed distribution has also been discussed. The modeling capability of the proposed model has been compared with seven classes of the lifetime distributions using real data. The generalizations of Weibull, exponential, Rayleigh, gamma, log normal, Pareto, Maxwell, Levy, Laplace, inverse gamma, Gompertz, chi-square, inverse chi-square, half normal, and log-logistic distributions have been considered for the comparison. The comparison has been made based on different goodness-of-fit criteria, such as Akaike information criteria (AIC), Bayesian information criteria (BIC), and Kolmogorov-Smirnov (KS) test. Based on the results from the study, it can be suggested that the BIID can efficiently replace commonly used lifetime distributions and their modifications. The results under this model were comparable with different conventional/modified distributions having up to six parameters.

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with β-cells and the other with no β-cell component to determine their capability in predicting glucose concentration and determine type 1 diabetes pathways using published glucose concentration data for four groups of experimental mice. The groups of mice were numbered Mice Group 1–4, depending on the diabetes severity of each group, with severity increasing from group 1–4. A Markov Chain Monte Carlo method based on a Bayesian framework was used to fit the model to determine the best model structure. Akaike information criteria (AIC) and Bayesian information criteria (BIC) approaches were used to assess the best model structure for type 1 diabetes. In fitting the model with no β-cells to glucose level data, we varied insulin absorption rate and insulin clearance rate. However, the model with β-cells required more parameters to match the data and we fitted the β-cell glucose tolerance factor, whole body insulin clearance rate, glucose production rate, and glucose clearance rate. Fitting the models to the blood glucose concentration level gave the least difference in AIC of 1.2, and a difference in BIC of 0.12 for Mice Group 4. The estimated AIC and BIC values were highest for Mice Group 1 than all other mice groups. The models gave substantial differences in AIC and BIC values for Mice Groups 1–3 ranging from 2.10 to 4.05. Our results suggest that the model without β-cells provides a more suitable structure for modelling type 1 diabetes and predicting blood glucose concentration for hypoglycaemic episodes.

Relative fat mass, a new index of adiposity, is strongly associated with incident heart failure: data from PREVEND

Body-mass index (BMI), waist circumference, and waist-hip ratio are commonly used anthropometric indices of adiposity. However, over the past 10 years, several new anthropometric indices were developed, that more accurately correlated with body fat distribution and total fat mass. They include relative fat mass (RFM), body-roundness index (BRI), weight-adjusted-waist index and body-shape index (BSI). In the current study, we included 8295 adults from the PREVEND (Prevention of Renal and Vascular End-Stage Disease) observational cohort (the Netherlands), and sought to examine associations of novel as well as established adiposity indices with incident heart failure (HF). The mean age of study population was 50 ± 13 years, and approximately 50% (n = 4134) were women. Over a 11 year period, 363 HF events occurred, resulting in an overall incidence rate of 3.88 per 1000 person-years. We found that all indices of adiposity (except BSI) were significantly associated with incident HF in the total population (P < 0.001); these associations were not modified by sex (P interaction > 0.1). Amongst adiposity indices, the strongest association was observed with RFM [hazard ratio (HR) 1.67 per 1 SD increase; 95% confidence interval (CI) 1.37–2.04]. This trend persisted across multiple age groups and BMI categories, and across HF subtypes [HR: 1.76, 95% CI 1.26–2.45 for HF with preserved ejection fraction; HR 1.61, 95% CI 1.25–2.06 for HF with reduced ejection fraction]. We also found that all adiposity indices (except BSI) improved the fit of a clinical HF model; improvements were, however, most evident after adding RFM and BRI (reduction in Akaike information criteria: 24.4 and 26.5 respectively). In conclusion, we report that amongst multiple anthropometric indicators of adiposity, RFM displayed the strongest association with HF risk in Dutch community dwellers. Future studies should examine the value of including RFM in HF risk prediction models.

Application of the ARIMA Model to Predict Under-Reporting of New Cases of Hansen’s Disease during the COVID-19 Pandemic in a Municipality of the Amazon Region

This work aimed to apply the ARIMA model to predict the under-reporting of new Hansen’s disease cases during the COVID-19 pandemic in Palmas, Tocantins, Brazil. This is an ecological time series study of Hansen’s disease indicators in the city of Palmas between 2001 and 2020 using the autoregressive integrated moving averages method. Data from the Notifiable Injuries Information System and population estimates from the Brazilian Institute of Geography and Statistics were collected. A total of 7035 new reported cases of Hansen’s disease were analyzed. The ARIMA model (4,0,3) presented the lowest values for the two tested information criteria and was the one that best fit the data, as AIC = 431.30 and BIC = 462.28, using a statistical significance level of 0.05 and showing the differences between the predicted values and those recorded in the notifications, indicating a large number of under-reporting of Hansen’s disease new cases during the period from April to December 2020. The ARIMA model reported that 177% of new cases of Hansen’s disease were not reported in Palmas during the period of the COVID-19 pandemic in 2020. This study shows the need for the municipal control program to undertake immediate actions in terms of actively searching for cases and reducing their hidden prevalence.

Mathematical Modeling of the growth of Acinetobacter baumannii YNWH 226 on Azo dye Congo red

Industrial effluents (Azo dyes) are brightly coloured, making their disposal into receiving waters undesirable not only because many Azo dyes and their breakdown products are toxic to aquatic life and mutagenic to humans, but also because many Azo dyes and their breakdown products are harmful to aquatic life due to the presence of aromatics and metals, chlorides, and other chemicals. Various kinetic models, including modified Gompertz, Baranyi-Roberts, modified Richards, Von Bertalanffy, modified Logistics, modified Schnute, Buchanan three-phase, and the most recently presented Huang, were used in this study. Based on statistical tests, the modified Schnute model provided the best fit, with the lowest values for RMSE and corrected Akaike Information Criteria (AICc), the greatest value for adjusted R2, and the closest to unity for both Accuracy and Bias Factor. The Modified Schnute parameters such as λ (lag time), µmax (maximum specific bacterial growth rate) and curve fitting parameters α and β (Constant), were found to be -4.39 (95% confidence interval of -77.58 to 68.79), 57.00 (95% confidence interval of -2854.30 to 2968.30), 0.78 (95% confidence interval of -0.34 to 1.89) and 0.96 (95% confidence interval of -0.85 to 2.78, respectively.

Intraoperative Use of Topical Retropharyngeal Steroids for Dysphagia after Anterior Cervical Fusion: A Systematic Review and Meta-Analysis

Purpose. The anterior cervical approach is commonly used clinically for cervical spondylosis, but it also results in frequent postoperative dysphagia, which can increase the risk of complications and poor treatment satisfaction in severe cases. Intraoperative local application of retropharyngeal steroids has an impact on reducing the occurrence and severity of dysphagia; however, the results of current studies vary. The meta-analysis of this randomized trial was to evaluate the effectiveness and safety of intraoperative topical retropharyngeal steroids for the control of dysphagia after anterior cervical spine surgery. Methods. Two authors searched electronic databases such as PubMed, MEDLINE, EMBASE, Cochrane Library, and Google Scholar, respectively. The search terms were “Dysphagia,” “Steroids,” “Anterior Cervical Discectomy and Fusion,” etc. A random effects model was used to conduct a meta-analysis based on deviance information criteria. Results. A total of 8 studies were included in this meta-analysis after screening of 792 studies. Bazaz scores were not significantly different in the steroid group at one day postoperatively ( P = 0.38 ), and dysphagia was significantly improved at 14 days postoperatively (95% CI: 0.15 to 0.64; P = 0.002 ). PSTSI was significantly improved one day ( P = 0.03 ) and 14 days after surgery ( P < 0.0001 ). VAS scores were all lower versus controls ( P < 0.001 ). Conclusion. Perioperative local retropharyngeal steroid administration as an adjunct to anterior cervical spine surgery reduces the incidence and severity of dysphagia compared with placebo control. However, future high-quality randomized controlled studies could incorporate nonsubjective dysphagia measures and long-term follow-up on the occurrence of associated complications or other side effects.

Mixture of Lindley and Lognormal Distributions: Properties, Estimation, and Application

Finite mixture models provide a flexible tool for handling heterogeneous data. This paper introduces a new mixture model which is the mixture of Lindley and lognormal distributions (MLLND). First, the model is formulated, and some of its statistical properties are studied. Next, maximum likelihood estimation of the parameters of the model is considered, and the performance of the estimators of the parameters of the proposed models is evaluated via simulation. Also, the flexibility of the proposed mixture distribution is demonstrated by showing its superiority to fit a well-known real data set of 128 bladder cancer patients compared to several mixture and nonmixture distributions. The Kolmogorov Smirnov test and some information criteria are used to compare the fitted models to the real dataset. Finally, the results are verified using several graphical methods.