Urban Road Accident Black Spot Identification and Classification Approach: A Novel Grey Verhuls–Empirical Bayesian Combination Method

The identification and classification of accident black spots on urban roads is a key element of road safety research. To solve the problems caused by the randomness of accident occurrences and the unclear classification of accident black spots by the traditional model, we propose a method that can quickly identify and classify accident black spots on urban roads: a combined grey Verhuls–Empirical Bayesian method. The grey Verhuls model is used to obtain the predicted/expected numbers of accidents at accident hazard locations, and the empirical Bayesian approach is used to derive two accident black spot discriminators, a safety improvement space and a safety index (SI), and to classify the black spots into two, three, four and five levels according to the range of the SI. Finally, we validate this combined method on examples. High-quality and high-accuracy data are obtained from the accident collection records of the Ningbo Jiangbei District from March to December 2020, accounting for 90.55% of the actual police incidents during this period. The results show that the combined grey Verhuls–Empirical Bayesian method can identify accident black spots quickly and accurately due to the consideration of accident information from the same types of accident locations. The accident black point classification results show that the five-level rating of accident black points is most reasonable. Our study provides a new idea for accident black spot identification and a feasible method for accident black spot risk level classification.

Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals

Burning velocity of different chemicals is estimated using a model from mixed population considering inverted Kumaraswamy (IKum) distribution for component parts. Two estimation techniques maximum likelihood estimation (MLE) and Bayesian analysis are applied for estimation purposes. BEs of a mixture model are obtained using gamma, inverse beta prior, and uniform prior distribution with two loss functions. Hyperparameters are determined through the empirical Bayesian method. An extensive simulation study is also a part of the study which is used to foresee the characteristics of the presented model. Application of the IKum mixture model is presented through a real dataset. We observed from the results that Linex loss performed better than squared error loss as it resulted in lower risks. And similarly gamma prior is preferred over other priors.

The effects of forest fires on hospital admissions for respiratory diseases

Objectives To analyze the effect of spatially exposure to fire risk on the occurrence of respiratory diseases in the municipalities of the State of Mato Grosso do Sul (MS), Brazil. Methods This was an ecological study of spatial prevalence of hospitalization for respiratory diseases and fire risk using Monte Alegre index. This methodology reduces the risk as precipitation occurs, where the volume of rain (in mm) is considered as danger lane changer. The empirical Bayesian method and a multiple regression spatial response variable were used to model the prevalence of hospitalization for respiratory diseases, and the exposure variable fire hazard. For calibration, the proxies of fire outbreaks and surface ozone concentration, precipitation and humidity were used. Results We observed statistically significant associations between the prevalence of hospitalization for respiratory diseases and the risk of fire. Conclusions fire risks triggering fires are highly related to the prevalence of hospitalizations for respiratory diseases in vulnerable sub populations in the municipalities of the State of Mato Grosso.

Spatial analysis of congenital syphilis in the State of Rio Grande do Norte, between 2008 and 2018

ABSTRACT Objective: To analyze the spatial distribution of congenital syphilis cases in a state in northeastern Brazil. Method: This is an ecological study, with secondary data for the period from 2008 to 2018, taking as a sample the notified cases of congenital syphilis in Rio Grande do Norte. In the data analysis, the eight health regions of the state were used as units of analysis, and the local and global Moran’s I was performed, with subsequent smoothing through the local empirical Bayesian method, which resulted in thematic maps. Results: The results showed an increase in cases of congenital syphilis in the 3rd and 7thhealth regions. In terms of spatial analysis, this investigation showed clusters in the 3rd, 5th, and 7thhealth regions, with an increased risk for congenital syphilis of up to 2.65 times and with an incidence rate of 7.91 cases per 1,000 live births. Conclusion: The spatial analysis of congenital syphilis cases allowed observing a high incidence in some health regions, with averages above those calculated for the entire state, indicating the need to implement effective strategies to achieve its control.

The Bühlmann–Straub Estimation of Claim Means in Random B-F Reserve Model

In the B-F reserve model, it is a very critical step to estimate the claim means of the accident year. However, the traditional method uses the prior estimators of the claim means based on the personal experience of actuaries or historical data. This method inevitably carries the subjectivity of the actuary himself. In this paper, a stochastic B-F model is established, and a prior distribution is constructed for the claim means in the accident year. The idea of the credibility theory is used to derive the linear Bayesian estimators of claim means. Finally, the empirical Bayesian method is used to estimate the first two moments of the prior distribution, and the empirical Bayesian estimators of the claim means and the corresponding reserves are derived. The estimators obtained in this paper do not depend on the specific forms of the sample distribution and the prior distribution and can be used directly in practice. In the numerical simulation, our estimates are compared with the traditional B-F estimates and the chain ladder estimates. It is verified that the estimates given in this paper have small mean square error.

Analysis of spatial clustering, time trend, social vulnerability and risk of human visceral leishmaniasis in an endemic area in Brazil: an ecological study

Background More than 95% of visceral leishmaniasis (VL) cases in Latin America occur in Brazil, most of them in the northeast. The objective of this study was to identify spatial clusters with the highest risks of VL and to analyse the temporal behaviour of the incidence and the effects of social vulnerability on the disease transmission dynamic in northeastern Brazil. Methods All confirmed cases registered as residents in the state of Pernambuco during the period from 2007 to 2017 were analysed. The local empirical Bayesian method was applied and the association -between the VL incidence rate and municipal social vulnerability was tested via classic multivariate regression. Results A total of 1186 new cases were registered during the study period. Spatial analysis showed heterogeneous distribution, with the highest rates observed in the São Francisco and Sertão mesoregions. Moreover, the main factors associated with VL were urban infrastructure, income and work. Conclusions It was observed that spatial and temporal techniques are important tools for defining risk areas for VL, in conjunction with the evaluation of indexes of social vulnerability, which was shown to be an important factor for comprehending associations with VL in the state of Pernambuco.

A Statistical Framework for Evolutionary Analysis of Recurrent Somatic Mutations in Cancers

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored, faciltating enormous high throuput analyses to explore the underlying mechanisms that may contribute to malignant initiation or progression. In the context of over-dominant passenger mutations (unrelated to cancers), the challenge is to identify somatic mutations that are cancer-driving. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model that enables to accomplish the following analyses. (i) We formulated a quasi-likelihood approach to test whether the two-component model is significantly better than a single-component model, which can be used for new cancer gene predicting. (ii) We implemented an empirical Bayesian method to calculate the posterior probabilities of a site to be cancer-driving for all sites of a gene, which can be used for new driving site predicting. (iii) We developed a computational procedure to calculate the somatic selection intensity at driver sites and passenger sites, respectively, as well as site-specific profiles for all sites. Using these newly-developed methods, we comprehensively analyzed 294 known cancer genes based on The Cancer Genome Atlas (TCGA) database.