scholarly journals Using Generalized Linear Mixed Models to Predict the Number of Roadway Accidents: A Case Study in Hamilton County, Tennessee

2020 ◽  
Vol 14 (1) ◽  
pp. 1-13
Author(s):  
Eric M. Laflamme ◽  
Peter Way ◽  
Jeremiah Roland ◽  
Mina Sartipi
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Weather Conditions ◽  
Residential Areas ◽  
Explanatory Variables ◽  
Accident Frequency ◽  
Interaction Terms ◽  
Rainy Weather ◽  
Accident Data ◽  
Hamilton County

Introduction: A method for identifying significant predictors of roadway accident counts has been presented. This process is applied to real-world accident data collected from roadways in Hamilton County, TN. Methods: In preprocessing, an aggregation procedure based on segmenting roadways into fixed lengths has been introduced, and then accident counts within each segment have been observed according to predefined weather conditions. Based on the physical roadway characteristics associated with each individual accident record, a collection of roadway features is assigned to each segment. A mixed-effects Negative Binomial regression form is assumed to approximate the relationship between accident counts and several explanatory variables including roadway characteristics, weather conditions, and several interactions between them. Standard diagnostics and a validation procedure show that our model form is properly specified and suitably fits the data. Results: Interpreting interaction terms leads to the follow findings: 1) rural roads with cloudy conditions are associated with relative increases in accident frequency; 2) lower/moderate AADT and rainy weather are associated with relative decreases in accident frequency, while high AADT and rain are associated with relative increases in accident frequency; 3) higher AADT and wider pavements are associated with relative increases in accident frequency; and 4) higher speed limits in residential areas are associated with relative increases in accident frequency. Conclusion: Results illustrate the complicated relationship between accident frequency and both roadway features and weather. Therefore, it is not sufficient to observe the effects of weather and roadway features independently as these variables interact with one another.

Behavioural characterisation of Holstein steers in three different production systems

2016 ◽  
Vol 56 (10) ◽  
pp. 1683 ◽  
Author(s):  
O. Blumetto ◽  
A. Ruggia ◽  
A. Dalmau ◽  
F. Estellés ◽  
A. Villagrá
Keyword(s):  
Correspondence Analysis ◽  
Negative Binomial ◽  
Production Systems ◽  
Negative Binomial Regression ◽  
Weather Conditions ◽  
Rainy Weather ◽  
Behavioural Patterns ◽  
Binomial Regression ◽  
Sunny Weather ◽  
Grazing Behaviour

The objective of the present study was to characterise the behaviour of Holstein steers in three different production systems. Forty-eight castrated Holstein males were randomly divided into three groups and allocated to the following three outdoor treatments: (T1) animals confined in a yard with an area of 210 m2, (T2) animals confined in a similar-sized yard but with 6 h of access to a pasture plot, (T3) animals maintained throughout the experiment on a pasture plot. Behaviour was recorded by scan sampling, 12 h a day (from 0700 hours to 1900 hours), 3 days per week, for 4 weeks evenly distributed from Week 7 to Week 16 of the experiment. So as to assess their patterns of behaviour, a negative binomial regression, correspondence analysis and logistic regressions were performed. Grazing was the predominant behaviour among Groups T2 and T3, while ‘eating hay’ was the most frequent behaviour among Group T1. For all treatments, lying was the second-most frequent behaviour. Despite animals in T2 having access to pasture for only half of the time with respect to those in T3, there was no difference between both treatments in the time spent grazing. Correspondence analysis of behaviour as a function of weather conditions showed that several behaviours were close to certain weather conditions, e.g. ‘standing’ and ‘ruminating while standing’ were closer to light rainy weather, while ‘lying’ or ‘ruminating while lying’ were more related to sunny weather.’Lying’ tended to increase along the day in all treatments, while ‘eating hay’ increased along the day within Group T1, but decreased within Groups T2 and T3. It is concluded that the management conditions associated with the systems that were studied produced different behavioural patterns in the steers. Grazing behaviour is important for the animals, and the permanent or restricted possibility to perform it, determined by the production system, meant that the patterns of other behaviours changed to give priority to pasture intake.

scholarly journals Ambient temperature and subsequent COVID-19 mortality in the OECD countries and individual United States

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Costas A. Christophi ◽  
Mercedes Sotos-Prieto ◽  
Fan-Yun Lan ◽  
Mario Delgado-Velandia ◽  
Vasilis Efthymiou ◽  
...  
Keyword(s):  
United States ◽  
Ambient Temperature ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Weather Conditions ◽  
Oecd Countries ◽  
Epidemiological Studies ◽  
Sociodemographic Factors ◽  
Sensitivity Analyses ◽  
Inverse Association

AbstractEpidemiological studies have yielded conflicting results regarding climate and incident SARS-CoV-2 infection, and seasonality of infection rates is debated. Moreover, few studies have focused on COVD-19 deaths. We studied the association of average ambient temperature with subsequent COVID-19 mortality in the OECD countries and the individual United States (US), while accounting for other important meteorological and non-meteorological co-variates. The exposure of interest was average temperature and other weather conditions, measured at 25 days prior and 25 days after the first reported COVID-19 death was collected in the OECD countries and US states. The outcome of interest was cumulative COVID-19 mortality, assessed for each region at 25, 30, 35, and 40 days after the first reported death. Analyses were performed with negative binomial regression and adjusted for other weather conditions, particulate matter, sociodemographic factors, smoking, obesity, ICU beds, and social distancing. A 1 °C increase in ambient temperature was associated with 6% lower COVID-19 mortality at 30 days following the first reported death (multivariate-adjusted mortality rate ratio: 0.94, 95% CI 0.90, 0.99, p = 0.016). The results were robust for COVID-19 mortality at 25, 35 and 40 days after the first death, as well as other sensitivity analyses. The results provide consistent evidence across various models of an inverse association between higher average temperatures and subsequent COVID-19 mortality rates after accounting for other meteorological variables and predictors of SARS-CoV-2 infection or death. This suggests potentially decreased viral transmission in warmer regions and during the summer season.

scholarly journals Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Aschalew Kassu ◽  
Michael Anderson
Keyword(s):  
Negative Binomial ◽  
Surface Condition ◽  
Negative Binomial Regression ◽  
Segment Length ◽  
Mean Values ◽  
Rural Highways ◽  
Explanatory Variables ◽  
Crash Data ◽  
Pavement Surface ◽  
Binomial Regression

This study examines the effects of wet pavement surface conditions on the likelihood of occurrences of nonsevere crashes in two- and four-lane urban and rural highways in Alabama. Initially, sixteen major highways traversing across the geographic locations of the state were identified. Among these highways, the homogenous routes with equal mean values, variances, and similar distributions of the crash data were identified and combined to form crash datasets occurring on dry and wet pavements separately. The analysis began with thirteen explanatory variables covering engineering, environmental, and traffic conditions. The principal terms were statistically identified and used in a mathematical crash frequency models developed using Poisson and negative binomial regression models. The results show that the key factors influencing nonsevere crashes on wet pavement surfaces are mainly segment length, traffic volume, and posted speed limits.

scholarly journals Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java

2021 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Yopi Ariesia Ulfa ◽  
Agus M Soleh ◽  
Bagus Sartono
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Explanatory Variables ◽  
Binomial Regression ◽  
Directorate General ◽  
The Republic ◽  
And Control

Based on data from the Directorate General of Disease Prevention and Control of the Ministry of Health of the Republic of Indonesia, in 2017, new leprosy cases that emerged on Java Island were the highest in Indonesia compared to the number of events on other islands. The purpose of this study is to compare Poisson regression to a negative binomial regression model to be applied to the data on the number of new cases of leprosy and to find out what explanatory variables have a significant effect on the number of new cases of leprosy in Java. This study's results indicate that a negative binomial regression model can overcome the Poisson regression model's overdispersion. Variables that significantly affect the number of new cases of leprosy based on the results of negative binomial regression modeling are total population, percentage of children under five years who had immunized with BCG, and percentage of the population with sustainable access to clean water.

scholarly journals Estimation of side-impact crash risk at signalized intersections

2021 ◽  
Author(s):  
Dewan Masul Karim
Keyword(s):  
Negative Binomial ◽  
Safety Management ◽  
Likelihood Estimation ◽  
Negative Binomial Regression ◽  
Signalized Intersections ◽  
Side Impact ◽  
Crash Risk ◽  
Estimation Of Parameters ◽  
Explanatory Variables ◽  
The Relationship

Side impact accidents are considered to be the most dangerous of all types of intersection accidents due to their high severity. In-depth investigation of accident occurrence could be a valuable means of mitigating these accidents. Based on the relationship between the distribution of disturbances near intersections and driers reactions, this study developed a logic for three major types of signalized intersections side impact crashes were considered for this study - right-angle, and left and right-turning crashes. The study developed models to understand the relationship between accident frequency and some explanatory variables that represent, driver, vehicle, traffic flow and intersection design characteristics. Negative binomial regression with maximum likelihood estimation of parameters is applied to address the overdispersion usually found in accident count data. The models explain the mechanism of side-impact accident occurrence and could be used to assist safety management agencies to devise countermeasures aimed at divers, and the physical roadway environment.

A Zero-Inflated Negative Binomial Regression Model to Evaluate Ship Sinking Accident Mortalities

2018 ◽  
Vol 2672 (11) ◽  
pp. 65-72
Author(s):  
Tian Chai ◽  
De-qi Xiong ◽  
Jinxian Weng
Keyword(s):  
Negative Binomial ◽  
Human Life ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Policy Makers ◽  
Cruise Ships ◽  
Binomial Regression ◽  
Accident Data ◽  
Hull Damage ◽  
Human Life Loss

Sinking accidents are a seafarer’s nightmare. Using 10 years’ of worldwide sinking accident data, this study aims to develop a mortality count model to evaluate the human life loss resulting from sinking accidents using zero-inflated negative binomial regression approaches. The model results show that the increase of the expected human life loss is the largest when a ship suffers a precedent accident of capsizing, followed by fire/explosion or collisions. Lower human life loss is associated with contact and machinery/hull damage accidents. Consistent with our expectation, cruise ships involved in sinking accidents usually suffer more human life loss than non-cruise ships and there is be a bigger mortality count for sinking accidents that occur far away from the coastal area/harbor/port. Fatalities can be less when the ship is moored or docked. The results of this study are beneficial for policy-makers in proposing efficient strategies to reduce sinking accident mortalities.

scholarly journals Educational inequalities in hospital care for mortally ill patients in Norway

2017 ◽  
Vol 46 (1) ◽  
pp. 74-82 ◽  
Author(s):  
Jon Ivar Elstad
Keyword(s):  
Fixed Effects ◽  
Causes Of Death ◽  
Negative Binomial ◽  
Hospital Admissions ◽  
Negative Binomial Regression ◽  
Educational Inequalities ◽  
Residential Areas ◽  
Geographical Differences ◽  
Medical Needs ◽  
Low Educated

Aims: Health care should be allocated fairly, irrespective of patients’ social standing. Previous research suggests that highly educated patients are prioritized in Norwegian hospitals. This study examines this contentious issue by a design which addresses two methodological challenges. Control for differences in medical needs is approximated by analysing patients who died from same causes of death. Area fixed effects are used for avoiding that observed educational inequalities are contaminated by geographical differences. Methods: Men and women who died 2009–2011 at age 55–94 were examined ( N=103,000) with register data from Statistics Norway and the Norwegian Patient Registry. Educational differences in quantity of hospital-based medical care during the 12–24 months before death were analysed, separate for main causes of death. Multivariate negative binomial regression models were estimated, with fixed effects for residential areas. Results: High-educated patients who died from cancers had significantly more outpatient consultations at somatic hospitals than low-educated patients during an average observation period of 18 months prior to death. Similar, but weaker, educational inequalities appeared for outpatient visits for patients whose deaths were due to other causes. Also, educational inequalities in number of hospital admissions were marked for those who died from cancers, but insignificant for patients who died from other causes. Conclusions: Even when medical needs are similar for mortally ill patients, those with high education tend to receive more medical services in Norwegian somatic hospitals than patients with low education. The roles played by physicians and patients in generating these patterns should be explored further.

Development and Validation of a Seven-County Regional Pedestrian Volume Model

2021 ◽  
pp. 036119812199236
Author(s):  
Robert J. Schneider ◽  
Andrew Schmitz ◽  
Xiao Qin
Keyword(s):  
Low Socioeconomic Status ◽  
Negative Binomial ◽  
Motor Vehicle ◽  
Negative Binomial Regression ◽  
Metropolitan Region ◽  
Future Research ◽  
Cube Root ◽  
Explanatory Variables ◽  
New Variables ◽  
Development And Validation

This study describes the development and validation of pedestrian intersection crossing volume models for the seven-county Milwaukee metropolitan region. The set of three models, among the first developed at a multi-county scale, can be used to estimate the total number of pedestrian crossings per year at four-leg intersections along state highways and other major thoroughfares. Outputs are appropriate for annual volumes ranging from 1,000 to 650,000. We used negative binomial regression to relate annual pedestrian volumes at 260 intersections to roadway and surrounding neighborhood socioeconomic and land-use variables. The three models include seven variables that have significant positive associations with annual pedestrian volume: population density within 400 m of the intersection; employment density within 400 m; number of bus stops within 100 m; number of retail businesses within 100 m; number of restaurant and bar businesses within 100 m; presence of a school within 400 m; and proportion of households without a motor vehicle within 400 m. Results suggest that square root or cube root transformations of continuous explanatory variables could potentially improve model fit. The models have fair accuracy, with each of the three model formulations predicting 60% or more of validation intersection counts to within half or double the observed value. Future research could address overprediction by creating new variables to better represent the number of lanes on each intersection leg and low socioeconomic status of adjacent neighborhoods.

scholarly journals Count Regression Models for Analyzing Crime Rates in The East Java Province

2021 ◽  
Vol 2123 (1) ◽  
pp. 012028
Author(s):  
Dian Handayani ◽  
A F Artari ◽  
W Safitri ◽  
W Rahayu ◽  
V M Santi
Keyword(s):  
Poisson Regression ◽  
Crime Rate ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Crime Rates ◽  
Poor People ◽  
Explanatory Variables ◽  
Generalized Poisson ◽  
Binomial Regression ◽  
Generalized Poisson Regression

Abstract Crime rate is the number of reported crimes divided by total population. Several factors could contribute the variability of crime rates among areas. This study aims to model the relationship between crime rates among regencies and cities in the East Java Province (Indonesia) and some potentially explanatory variables based on Statistics Indonesia publication in 2020. The crime rate in the East Java Province was consistently at the top three after DKI Jakarta and North Sumatra during 2017 to 2019. Therefore, it is interesting for us to study further about the crime rate in the East Java. Our preliminary analysis indicates that there is an overdispersion in our sample data. To overcome the overdispersion, we fit Generalized Poisson and Negative Binomial regression. The ratio of deviance and degree of freedom based on Negative Binomial is slightly smaller (1.38) than Generalized Poisson (1.99). The results indicate that Negative Binomial and Generalized Poisson regression, compared to standard Poisson regression, are relatively fit to model our crime rate data. Some factors which contribute significantly (α=0.05) for the crime rate in the East Java Province under Negative Binomial as well as Generalized Poisson regression are percentage of poor people, number of households, unemployment rate, and percentage of expenditure.

Predicting Dengue Hemorraghic Fever Cases in Metro Manila, Philippines

2020 ◽  
Author(s):  
Imee Necesito ◽  
Jaewon Jung ◽  
Young Hye Bae ◽  
Soojun Kim ◽  
Hung Soo Kim
Keyword(s):  
Principal Components ◽  
Negative Binomial ◽  
Moving Average ◽  
Principal Component ◽  
Negative Binomial Regression ◽  
The Philippines ◽  
Dengue Case ◽  
Explanatory Variables ◽  
Binomial Regression ◽  
Metro Manila

<p>Researchers have been looking for methods to prevent, control and provide lifelong protection to humans against dengue disease which is brought by the dengue-carrying mosquito called the Aedes Aegypti. However, such prevention, control and protection will best be aided by a dengue case prediction model. This study used the Negative Binomial Regression to forecast the dengue case incidence in Metro Manila, Philippines using principal components as explanatory variables. To ensure that the dengue cases are predictable, close returns plot (CRP) was performed.   The logarithm of dengue case incidence which were assigned as response variables have showed higher value of variance over the mean which validates the use of negative binomial regression. Principal Component Analysis utilizing Nino 3.4 sea surface temperature (SST), precipitation and minimum temperature was used in the study. The acquired principal components (PC1, PC2, PC3 and PC4) were used as the explanatory variables for the negative binomial regression to calculate the number of the logarithm of dengue case incidence. However, to improve the calculated value of DHF cases in comparison to its actual value, residuals from the negative binomial regression were treated using moving average approach. The data used in this study were from 1994-2010 climatological data. Results for both negative binomial and moving average were combined to get the forecasted dengue incidence. Forecasted values showed a maximum of 12% difference from the actual DHF cases indicating a high forecasting performance. This study which focused on predicting the possible dengue incidence in the central districts of the Philippines  is believed to be essential to create plans of action to prevent and control this disease.</p>

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