Bivariate ordered probit modelling of motorcycle riders and pillion passengers' injury severities relationship and associated risk factors.

This study simultaneously modelled the injury severity of motorcycle riders and their pillion passengers and determine the associated risk factors. The analysis is based on motorcycle crashes data in Ashanti region of Ghana spanning from 2017 to 2019. The study implemented bivariate ordered probit model to identify the possible risk factors under the premise that the injury severity of pillion passenger is endogenously related to that of the rider in the event of crash. The model provides more efficient estimates by considered the common unobserved factors shared between rider and pillion passenger. The result shows a significant positive relationship between the two injury severities with a correlation coefficient of 0.63. Thus, the unobservable factors that increase the probability of the rider to sustain more severe injury in the event of crash also increase that of their corresponding pillion passenger. The rider and their pillion passenger injury severities have different propensity to some of the risk factors including passengers' gender, day of week, road width and light condition. In addition, the study found that time of day, weather condition, collision type, and number of vehicles involved in the crash jointly influence the injury severity of both rider and pillion passenger significantly.

Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model.

Prediction of COVID-19 incidence and transmissibility rates are essential to inform disease control policy and allocation of limited resources (especially to hotspots), and also to prepare towards healthcare facilities demand. This study demonstrates the capabilities of nonlinear smooth transition autoregressive (STAR) model for improved forecasting of COVID-19 incidence in the Africa sub-region were investigated. Data used in the study were daily confirmed new cases of COVID-19 from February 25 to August 31, 2020. The results from the study showed the nonlinear STAR-type model with logistic transition function aptly captured the nonlinear dynamics in the data and provided a better fit for the data than the linear model. The nonlinear STAR-type model further outperformed the linear autoregressive model for predicting both in-sample and out-of-sample incidence.

Geographically weighted principal component analysis for characterising the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana.

The use of principal component analysis (PCA) for soil heavy metals characterization provides useful information for decision making and policies regarding the potential sources of soil contamination. However, the concentration of heavy metal pollutants is spatially heterogeneous. Accounting for such spatial heterogeneity in soil heavy metal pollutants will improve our understanding with respect to the distribution of the most influential soil heavy metal pollutants. In this study, geographically weighted principal component analysis (GWPCA) was used to describe the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana. The results from the conventional PCA revealed that three principal components cumulatively accounted for 86% of the total variation in the soil heavy metals in the study area. These components were largely dominated by Fe and Zn. The results from the GWPCA showed that the soil heavy metals are spatially heterogeneous and that the use of PCA disregards this considerable variation. This spatial heterogeneity was confirmed by the spatial maps constructed from the geographically weighted correlations among the variables. After accounting for the spatial heterogeneity, the proportion of variance explained by the three geographically weighted principal components ranged between 85% and 89%. The first three identified GWPC were largely dominated by Fe, Zn and As, respectively. The location of the study area where these variables are dominated provides information for remediation.

The effects of weather on the spread of COVID-19: evidence from Ghana.

BACKGROUND: Climatic factors have been shown to influence communicable disease dynamics especially in tropical regions where temperature could swing from extreme heat and dryness to wet and cold within a short period of time. This is more pronounced in the spread of airborne diseases. In this study, the effect of some local weather variables (average temperature, average relative humidity, average wind speed and average atmospheric pressure) on the risk of Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Ghana is investigated. The daily confirmed new COVID-19 cases were compiled from the Ghana Health Service and the weather data extracted from Weatherbase. The type of relationship between the climatic variable and risk of spread were explored using the Generalized Additive Model (GAM). RESULTS: Results obtained showed that wind speed and atmospheric pressure have positive linear relationship with the spread of infection an increase in the risk of COVID-19 spread. In addition, the risk of spread fluctuates for temperature between 24 and 29 °C but sharply decreases when average temperature exceeds 29 °C. The risk of spread of COVID-19 significantly decrease for relative humidity between 72 and 76% and leveled afterwards. CONCLUSION: The results indicate that wind speed and pressure have a positive linear relationship with the risk of spread of COVID-19 whilst temperature and humidity have a non-linear relationship with the spread of COVID-19. These findings highlight the need for policy makers to design effective countermeasures for controlling the spread as we are still within the low temperature season.

Modelling risk factors for red light violation in the Kumasi Metropolis, Ghana.

Red light running places the violator and other road users at risk of road traffic crash. The aim of this research was to undertake a baseline study to establish the current rate of red light running in the Kumasi Metropolis, Ghana and to determine the associated risk factors. An uninterrupted road side observational survey was conducted at 10 signalized intersections using pro-forma checklist. A binary logit model was employed to determine the risk factors associated with traffic light violations. Overall, drivers were observed running the red light in 35% of all the red phases studied. From the statistical model, red light running was found to be influenced by the age and gender of the driver, presence of a passenger in the vehicle, vehicle type, junction type, cycle length of the signal and queue length. There is a need for targeted public awareness campaigns on the dangers of red light running. The education on red light violation must be accompanied by sustained Police enforcement of the traffic law to reduce the rate of violation. Automatic surveillance cameras should be installed at all critical signalized intersections to supplement Police efforts to enforce traffic safety laws and regulations.

A bivariate probit analysis of child passenger's sitting behaviour and restraint use in motor vehicle.

Motor vehicle injuries are a leading cause of death among children worldwide, though many of these deaths are preventable. Buckling young children in age- and size-appropriate car seats, booster seats, or seat belts and also seating them in appropriate position can lead to a significant reduction of serious and fatal injuries. This study investigated sitting behaviour and restraint use among child passengers through cross-sectional observational surveys conducted in Kumasi, Ghana. A bivariate probit model was developed for simultaneous determination of the contributing factors influencing child passenger's sitting behaviour and restraint use. The results showed that 26% of the child passengers observed were occupying the front seat and the prevalence rate of restraint use was 4.5%. The developed bivariate probit model clearly highlights the existence of interrelationship between child passenger's sitting position and restraint use. The key factors simultaneously influencing child passenger's sitting position and restraint use include vehicle type, driver's gender, driver's belt use, child's age, and the presence of other child or adult passenger. Furthermore, time of day and day of week also influence child passenger sitting behaviour but not their restraint use. These findings provide insight for better understanding of child transporting practices and the contributing factors influencing their sitting behaviour and restraint use. The findings also highlight the need for policy makers to design effective countermeasures to promote rear sitting and restraint use among child passengers.

The effect of natural and built environmental characteristics on pedestrian-vehicle crash severity in Ghana.

Despite the benefits of walking as a means of travelling, walking can be quite hazardous. Pedestrian-vehicle crashes remain a major concern in Ghana as they account for the highest percentage of fatalities. The objective of this study is to determine the effect of both natural and built environmental features on pedestrian-vehicle crash severity in Ghana. The study is based on an extensive pedestrian-vehicle crash dataset extracted from the National Road Traffic Accident Database at the Building and Road Research Institute (BRRI) of the Council for Scientific and Industrial Research (CSIR), Ghana. Using a multinomial logit modelling framework, possible determinants of pedestrian-vehicle crash severity were identified. The study found that fatal crashes are likely to occur during unclear weather conditions, on weekends, at night time where there are no lights, on curved and inclined roads, on untarred roads, at mid-blocks and on wider roads. The developed model and its interpretations will make important contributions to road crash analysis and prevention in Ghana with the possibility of extension to other developing countries. These contributing factors could inform policy makers on road design and operational improvements.

Evaluation of geostatistical estimators and their applicability to characterise the spatial patterns of recreational fishing catch rates.

Western Australians are heavily engaged in recreational fishing activities with a participation rate of approximately 30%. An accurate estimation of the spatial distribution of recreational catch per unit effort (catch rates) is an integral component for monitoring fish population changes and to develop strategies for ecosystem-based marine management. Geostatistical techniques such as kriging can provide useful tools for characterising the spatial distributions of recreational catch rates. However, most recreational fishery data are highly skewed, zero-inflated and when expressed as ratios are impacted by the small number problem which can influence the estimates obtained from the traditional kriging. The applicability of ordinary, indicator and Poisson kriging to recreational catch rate data was evaluated for three aquatic species with different behaviours and distribution patterns. The prediction performance of each estimator was assessed based on cross-validation. For all three species, the accuracy plot of the indicator kriging (IK) showed a better agreement between expected and empirical proportions of catch rate data falling within probability intervals of increasing size, as measured by the goodness statistic. Also, indicator kriging was found to be better in predicting the latent catch rate for the three species compared to ordinary and Poisson kriging. For each species, the spatial maps from the three estimators displayed similar patterns but Poisson kriging produced smoother spatial distributions. We show that the IK estimator may be preferable for the spatial modelling of catch rate data exhibiting these characteristics, and has the best prediction performance regardless of the life history and distribution patterns of those three species.