Total Fatalities Research Articles

Exploring factors influencing pedestrian accident severity: a multi-source study in the city of Berlin

BackgroundRoad safety remains a critical concern in Europe’s transport sector. In urban areas, where 40% of total road fatalities occur, pedestrians are particularly vulnerable, since according to road safety data accidents involving a pedestrian are 2.8 and 15 times more probable to be fatal than in rural areas and motorways respectively. In line with the Vision Zero concept and the European Commission’s directives, many EU members are taking steps to improve road safety. Accordingly, the identification of the key factors behind pedestrian accident occurrence and pedestrian accident severity in urban areas is ever more relevant.MethodologyThe proposed methodology employs traditional logistic regression models and artificial neural networks, using accident data from Berlin, Germany, sourced from the Berlin Open Data portal. The dataset comprises information on 3,257 accidents involving pedestrians in 2018 or 2019, including details about involved vehicles, accident details, and injury severity information. Additionally, the dataset was augmented with average network speed data from Uber Movement and road network information from Geographic Information System (GIS) applications.ResultsFormal analysis results indicated several factors as significant to accident severity, such as involvement of bicycle or heavy vehicles, lighting conditions, speed limit and accident type. Additionally, a comparison between modelling approaches shows a clear performance advantage of ANNs over statistical models. Research findings provide insight for various stakeholders working to enhance pedestrian safety in urban areas.

Road crash dynamics in Malaysia: Analysis of trends and patterns

Road crashes represent a significant public health and safety concern globally, and Malaysia is no exception. Understanding the trends and patterns of road crashes is essential for devising effective strategies to mitigate risks and enhance road safety. This study presents a comprehensive analysis of road crash dynamics, focusing on road users, severity patterns and geographical patterns in Malaysia from 2012 to 2022. Data sourced from the Royal Malaysian Police (RMP) are utilized to examine various aspects of road crashes. Road crash trend, geographical patterns, linear trend analysis and K-means clustering are employed to explore patterns of road crash in Malaysia. The findings reveal that motorcycles consistently emerged as the most involved road user. Geographical patterns discovered that Selangor exhibits higher crash number. Linear trend analysis revealed significant upward trends in crash frequency prior to the pandemic, while the number of fatalities resulting from road crash showed a downward trend over the observed period. K-means clustering identified that Selangor recorded high total crashes and high total of fatalities. This study also considers the influence of the Covid-19 pandemic on road crash dynamics, highlighting changes in travel patterns and behaviour. There also have been notable successes, such as the reduction in total fatalities and the effectiveness of targeted interventions via the accomplishments of initiatives of Malaysian Road Safety Plan 2014–2022.

Incompleteness of natural disaster data and its implications on the interpretation of trends

ABSTRACT We use data from a leading global database, primarily the reported numbers of fatal ‘hydrological’, ‘meteorological’ and ‘geophysical’ disasters and associated fatalities, to elucidate long-term trends in natural disaster impact. There are very strong upward trends in the number of reported disasters. However, we show that these trends are strongly biased by progressively improving reporting. We apply well-established methods based on analysing evolving differences in the patterns of large (many fatalities) to small (fewer fatalities) events to assess and quantify this bias. For all disaster types investigated, the long-term (multi-decade) true number of disasters appears to be unexpectedly stable over time. There was, however, a clear temporary increase between 1980 and 1990 for hydrological and meteorological disasters. Importantly, there has been a large long-term decline in total fatalities reported for weather-related disasters, attributable to very effective mitigation for larger disasters. Probably due to improved mitigation rather than reduced hazard, data for the most recent decades shows a systematic decrease in the number of larger (more fatality) weather-related disasters compared to smaller ones. In contrast, for the geophysical disasters, predominantly earthquakes, the relative numbers of larger versus smaller disasters, are fairly constant, as are total fatalities.

A decade of curtailment studies demonstrates a consistent and effective strategy to reduce bat fatalities at wind turbines in North America

Abstract There is a rapid, global push for wind energy installation. However, large numbers of bats are killed by turbines each year, raising concerns about the impacts of wind energy expansion on bat populations. Preventing turbine blades from spinning at low wind speeds, referred to as curtailment, is a method to reduce bat fatalities, but drawing consistent inference across studies has been challenging. We compiled publicly available studies that evaluated curtailment at six wind energy facilities in North America across 10 years. We used meta‐regression of 29 implemented treatments to determine fatality reduction efficacy as well as sources of variation influencing efficacy. We also estimated species‐specific fatality reduction for three species that comprise most fatalities in North America: hoary bat (Lasiurus cinereus), eastern red bat (Lasiurus borealis) and silver‐haired bat (Lasionycteris noctivagans). We found that curtailment reduced total bat fatalities by 33% with every 1.0 ms−1 increase in curtailment wind speed. Estimates of the efficacy for the three target species were similar (hoary bats: 28% per ms−1, 95% CI: 0.4%–48%, eastern red bats: 32% per ms−1, 95% CI: 13%–47% and silver‐haired bats: 32% per ms−1, 95% CI: 3%–53%). Across multiple facilities and years, a 5.0 ms−1 cut‐in speed was estimated to reduce total bat fatalities by an average of 62% (95% CI: 54%–69%). Mortality reductions at individual facilities in any given year were estimated to fall between 33%–79% (95% prediction interval). Inter‐annual differences rather than inter‐site or turbine characteristics accounted for most of the variation in efficacy rates. Species‐specific average mortality reduction at 5.0 ms−1 curtailment wind speed was 48% (95% CI: 24%–64%) for hoary bats, 61% (95% CI: 42%–74%) for eastern red bats and 52% (95% CI: 30%–66%) for silver‐haired bats. Practical implication. curtailment reduced bat mortality at wind turbines in this North American study. Efficacy increased proportionally as curtailment speed is raised, and patterns and rates of efficacy were similar across species. This indicates that curtailment is an effective strategy to reduce bat fatalities at wind energy facilities, but exploration of further refinements could both minimize bat mortality and maximize energy production.

Development of a Real-Time Dashboard for Overdose Touchpoints: User-Centered Design Approach.

Overdose Fatality Review (OFR) is an important public health tool for shaping overdose prevention strategies in communities. However, OFR teams review only a few cases at a time, which typically represent a small fraction of the total fatalities in their jurisdiction. Such limited review could result in a partial understanding of local overdose patterns, leading to policy recommendations that do not fully address the broader community needs. This study explored the potential to enhance conventional OFRs with a data dashboard, incorporating visualizations of touchpoints-events that precede overdoses-to highlight prevention opportunities. We conducted 2 focus groups and a survey of OFR experts to characterize their information needs and design a real-time dashboard that tracks and measures decedents' past interactions with services in Indiana. Experts (N=27) were engaged, yielding insights on essential data features to incorporate and providing feedback to guide the development of visualizations. The findings highlighted the importance of showing decedents' interactions with health services (emergency medical services) and the justice system (incarcerations). Emphasis was also placed on maintaining decedent anonymity, particularly in small communities, and the need for training OFR members in data interpretation. The developed dashboard summarizes key touchpoint metrics, including prevalence, interaction frequency, and time intervals between touchpoints and overdoses, with data viewable at the county and state levels. In an initial evaluation, the dashboard was well received for its comprehensive data coverage and its potential for enhancing OFR recommendations and case selection. The Indiana touchpoints dashboard is the first to display real-time visualizations that link administrative and overdose mortality data across the state. This resource equips local health officials and OFRs with timely, quantitative, and spatiotemporal insights into overdose risk factors in their communities, facilitating data-driven interventions and policy changes. However, fully integrating the dashboard into OFR practices will likely require training teams in data interpretation and decision-making.

Assessment of knowledge, attitudes, and practices regarding cardiovascular diseases (CVDs) among older individuals of rural Bangladesh: findings from a face-to-face interview

IntroductionCardiovascular diseases (CVDs) stand as the foremost contributor to global mortality, claiming roughly 17.9 million lives each year, constituting 32.1% of total fatalities. Their impact is notably profound in economies such as Bangladesh, exacting a substantial economic burden. Consequently, grasping the landscape of knowledge, attitudes, and practices is essential for timely identification and prevention strategies.MethodsThis cross-sectional study, carried out between January and May 2023 in the rural regions of Zirani, Savar Upazila, Dhaka, Bangladesh, utilized convenient sampling and conducted face-to-face interviews using a semi-structured questionnaire. It encompassed socio-demographic factors, as well as knowledge, attitudes, and practices concerning CVDs. Data analysis employed descriptive statistics, chi-square tests, and regression analyses, utilizing both the R programming language and SPSS (Version 26).ResultA total of 424 participants aged 60 years and above were included. The majority were male (60.8%), and the mean age was 71.21 ± 9.21 years, 57.3% were between 60 and 70 years old. Factors such as education, monthly family income, high blood pressure, diabetes, and non-smoking. Were significantly associated with higher knowledge, attitudes and practices scores.ConclusionThis study illuminates CVD-related KAP among rural Older Individuals in Bangladesh, revealing significant associations between factors such as education, monthly family income, high blood pressure, and non-smoking, with higher scores in knowledge, attitudes, and practices regarding cardiovascular health. These insights underscore the importance of addressing socio-economic factors and health behaviors in developing targeted interventions for the prevention and management of cardiovascular diseases in this demographic.

Investigating and modeling motorcycle crash rates at urban unsignalized intersections in India

Objective The present study focuses on understanding the behavior of motorized 2-wheeler (MTW) riders at urban unsignalized intersections in India. In the Indian context, over 60% of road crash fatalities are attributed to vulnerable road users, with MTWs serving as the predominant contributors, accounting for 44% of total fatalities. Notably, unsignalized intersections have emerged as critical sites for accidents involving vulnerable road users. Methods Postencroachment time is used to assess traffic conflicts of MTW users. Furthermore, the study employs the exceedance property of extreme value theory to calculate crash probabilities. Tobit and grouped random parameters Tobit regression models are developed to model crash probabilities, incorporating variables such as traffic volume, traffic composition, gap acceptance time, intersection characteristics, and intersection conflict area at 4 urban unsignalized intersections in Surat, India. Results MTW riders have the lowest gap acceptance time among vehicles in the traffic stream. Cars and other heavy vehicles readily accept gaps when MTWs are in the conflicting stream at unsignalized intersections, which increases traffic conflicts. MTWs have the highest crash rates in the traffic stream. Among the developed models, the grouped random parameters Tobit regression captures the spatial unobserved heterogeneity of the study sites and outperforms the simple Tobit regression model. The results also indicate that MTW riders are exposed to a higher risk of crashes while turning at unsignalized intersections. The presence of a central traffic island has varied implications; it raises crash rates at 3-legged intersections but lowers them at 4-legged intersections for 2-wheelers. Conclusion The study concludes that MTW crash rates are influenced by traffic and intersectional factors. Increased gap acceptance time correlates with lower crash rates. Countermeasure selections require detailed investigations, because it was observed that the presence of central traffic islands has varied effects on crash rates at 3-legged and 4-legged unsignalized intersections.

Breast cancer diagnosis model using stacked autoencoder with particle swarm optimization

Breast cancer (BrC) stands as the most prevalent cancer affecting women globally, comprising 24.5% of all female cancer diagnoses and contributing to 15.0% of total cancer-related fatalities. The timely detection and precise categorization of breast cancer play pivotal roles in enhancing patient prognosis and treatment outcomes. The main goal is to enhance the precision of classifying mammogram images, thus offering vital support to radiology experts in diagnosing BrCs. The proposed model encompasses several pivotal stages, including pre-processing, feature extraction, segmentation, and classification. To assess the model's efficacy, we employed the INBreast dataset. During pre-processing, mammogram images were enhanced through a customized contrast-limited adaptive histogram equalization (mCLAHE) technique coupled with data augmentation. Segmentation was executed utilizing the Res-SegNet model, and feature extraction employing the VGG-19 model. The classification was conducted via a stacked autoencoder (SAE) with particle swarm optimization (PSO). Our proposed model exhibited notably high performance compared to alternative models such as CNN, Yolo-v4, and Inception-v3. The results unveiled an accuracy of 98.33%, precision of 99.39%, recall of 98.78%, specificity of 93.75%, an F1-score of 99.08%, and an MCC score of 90.04%.

Evaluating the Effectiveness of Speed Cameras on Philadelphia’s Roosevelt Boulevard

This paper employs Bayesian negative binomial and Poisson models to examine the effectiveness of speed cameras at reducing crashes, injuries, and fatalities on and immediately around Roosevelt Boulevard, a particularly dangerous urban arterial in Philadelphia, PA. Camera installation also coincided with rapidly changing traffic safety outcomes throughout the summer after COVID-19 lockdowns and protests against police violence. To find the most appropriate control segments for our analysis, we use k-means clustering to identify the five most similar roadways. Compared with these roadways and other geographic controls, the treated sections of Roosevelt Boulevard experienced decreased crashes, injuries, and fatalities after camera installation. These decreases are on the high end of what is found in the academic literature on speed cameras. The findings for total crashes, traffic injuries, fatalities, and pedestrian injuries are statistically different from zero with a high degree of confidence. Differences in pedestrian fatalities are statistically different from zero with 90% confidence. Based on our findings, we recommend that the Commonwealth of Pennsylvania and City of Philadelphia extend and expand the automated speed enforcement pilot. Estimates of the economic benefits of reduced crashes substantially exceed the total fines paid by violators. Despite this positive safety effect, additional treatments will be required to make Roosevelt Boulevard substantially safer. The roadway still accounts for around 8% of all traffic fatalities in Philadelphia.

Toxicity Risk Assessment Due to Particulate Matter Pollution from Regional Health Data: Case Study from Central Romania.

Air pollution poses one of the greatest dangers to public well-being. This article outlines a study conducted in the Central Romania Region regarding the health risks associated with particulate matter (PM) of two sizes, viz., PM10 and PM2.5. The methodology used consists of the following: (i) an analysis of the effects of PM pollutants, (ii) an analysis of total mortality and cardiovascular-related mortality, and (iii) a general health risk assessment. The Central Region of Romania is situated in the Carpathian Mountains' inner arch (consisting of six counties). The total population of the region under investigation is about 2.6 million inhabitants. Health risk assessment is calculated based on the relative risk (RR) formula. During the study period, our simulations show that reducing these pollutants' concentrations below the new WHO guidelines (2021) will prevent over 172 total fatalities in Brasov alone, as an example. Furthermore, the potential benefit of reducing annual PM2.5 levels on total cardiovascular mortality is around 188 persons in Brasov. Although health benefits may also depend upon other physiological parameters, all general health indicators point towards a significant improvement in overall health by a general reduction in particulate matter, as is shown by the toxicity assessment of the particulate matter in the region of interest. The modality can be applied to other locations for similar studies.

Pathological characteristics of lung tumors in Sri Lanka 2017-2021.

The prevalence of lung cancer is steadily increasing globally, and it is projected to become the second most prevalent cancer in men by 2030. Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for approximately 3.61% of total fatalities. Despite its significant impact, many Asian countries, including Sri Lanka, lack precise data on the epidemiological patterns of lung tumors. This study pioneers a comprehensive exploration in Sri Lanka, delving into the demographic and clinicopathological characteristics of lung cancer patients. The study included 733 consecutive patients with lung tumors from 2017 to 2021, with a median age of 59 years. The most common site of tumors was the right lower lobe and left upper lobes. Adenocarcinoma was the most prevalent histopathological type of primary malignant lung tumors, while colorectal adenocarcinomas were the most common cause of metastatic deposits in the lungs. The most common benign tumor was hamartoma. Significantly, our findings unveiled associations between patient demographics and tumor types, underscoring the importance of factoring in age and gender in diagnostic assessments. Notably, the absence of a dedicated lung cancer screening program in Sri Lanka underscores the critical reliance on clinical suspicion and accurate diagnostic methods.

The Relationship Between the Atmospheric Environment and Road Traffic Fatalities - Shandong Province, China, 2012-2021.

This study aims to analyze the potential impact of the meteorological environment and air pollutants on road traffic fatalities. Road traffic fatality data in Shandong Province from 2012 to 2021 were obtained from the Population Death Information Registration Management System. Meteorological and air pollutant data for the same period were collected from the U.S. National Oceanic and Atmospheric Administration and the Ecological Environment Monitoring Center of Shandong Province, China. Pearson's correlation and ridge regression were used to analyze the impact of the meteorological environment and air pollutants on road traffic fatalities. From 2012 to 2021, there were 163,863 road traffic fatality cases. The results of the ridge regression analysis showed that the daily average temperature was negatively correlated with total fatalities and passengers and positively correlated with pedestrians, nonmotorized drivers, and motorized drivers. The daily minimum temperature was negatively correlated with total fatalities and positively correlated with motorized drivers. The daily maximum temperature was positively correlated with both pedestrian and nonmotorized drivers. The daily accumulated precipitation was negatively correlated with pedestrians. Sunshine duration was positively correlated with both nonmotorized and motorized drivers. Inhalable particulate matter (PM10) and nitrogen dioxide (NO2) were positively correlated with total fatalities, pedestrians, and nonmotorized drivers. Sulfur dioxide (SO2) was positively correlated with total fatalities but negatively correlated with nonmotorized drivers, passengers, and motorized drivers. Atmospheric factors associated with the occurrence of road traffic fatalities include air temperature, daily accumulated precipitation, sunshine duration, and air pollutants such as PM10, NO2, and SO2.

A Comparative Study of Terrorist Attacks Targeting Healthcare in North America vs Europe

Abstract Objective This study sought to compare terrorist attacks targeting healthcare in two different geographical areas that are economically and culturally similar: North America and Europe. Methods The Global Terrorism Database is a database that documents 214,666 worldwide terrorist events from 1970 to 2021. It was searched to identify terrorist attacks against healthcare using keywords. We manually excluded incidents that did not specifically relate to attacks on healthcare. This resulted in 311 and 150 entries in North America and Europe respectively. Results The most common method of attack in Europe was bombing (42%) compared to attacks on infrastructure (66%) in North America. The primary target in North America was abortion related (84%). In Europe, the primary target was businesses (37%). In Europe, 18.7% of attacks were assassinations or attempts vs 3% in North America. The total number of fatalities in Europe is 51 vs 3 in North America. Conclusion Even though there were significantly more attacks in North America, there were far fewer total fatalities compared to Europe. Attacks in North America appear to be ideological, targeting infrastructure to send a message. Whereas, in Europe, the targets and methods used are more targeted towards the individual and their property.

Motorcyclists Preferred Lane Position on Federal Roads in Malaysia

Motorcycle fatalities comprised of 60% of the total fatalities in Malaysia. Weaving between traffic and speeding are among the contributors to the high motorcycle fatalities and there is not much understanding of the. This study was conducted with the objective to determine the lane preference of motorcyclists within various road cross-section and speed of motorcyclists when using the lanes. The lane usage was recorded with video camera and video was playback to count the count of motorcyclists using their respective lane. As for speed of motorcyclists, spot speed measurement was conducted covertly to avoid affecting motorcyclists’ behaviour. A total of 11 locations with different cross-section designs throughout the Malaysia was identified for observation. The empirical results show that more than 95% of the motorcyclists travelled on the non-exclusive motorcycle lane (NEML) when the NEML is presence at 2 lane single carriageway section. Meanwhile, paved shoulder about 2.2-2.3m wide was found to be used by majority of motorcyclists (75%). Apart from obtaining the lateral travel position of the motorcyclists, the spot speed data were also collected. The speed results revealed that motorcyclists travelled statistical significantly slower on NEML or Paved shoulder (PS) than on main travel lane. The findings of this study conclude that NEML and paved shoulder can affect motorcyclists to ride slower and thus enhance the safety of motorcyclists in term of speed exposure. In addition, the NEML and PS separate motorcyclists from other motorized vehicles which could reduce the risk of crashes of motorcyclists with another vehicle. Even though provision of NEML incurs high costs, however in long term, NEML would prove to be one of the most efficient allocation resources in reducing motorcycle crashes. However, further studies need to be carried out on the use of paved shoulder by motorcyclists.

Scorpion stings envenomation in Sudan: a retrospective study of hospital-based incidence

Scorpion stings are a common medical emergency in many parts of the world. However, no statistical records are available about the incidence of scorpion sting envenomation and related mortality rates in the Sudan. This study aimed to describe the burden in the Sudan and to highlight the most affected states. We extracted retrospective hospital-based data on scorpions’ stings envenomation for the years 2014–2018 from the annual statistical reports of the Federal Ministry of Health, Republic of the Sudan. Descriptive statistics were used to analyze the extracted data and elucidate the results. A total of 129,427 people were envenomed during 2014–2018 with a mean of 25,885 cases/year. More adults were stung compared to children. However, mortality was higher among children younger than 15 years old 4.7% (186/3974) than older victims 1% (56/5371). Envenomation was more frequent in males (56%) than females (44%). The Northern state reported the highest incidence of scorpion stings (344 per 100,000 population), followed by the River Nile state (240 per 100,000 population), and Khartoum state (174 per 100,000 population). Fatalities amongst hospitalized patients were 2.6% (242/9345) with the Northern state accounting for 34% (0.8/2.3) of total fatalities reported.

Analysis of road accident mortality based on time of occurrence for Kerala, India

Background: Road traffic accidents (RTA) pose a significant socio-economic burden and global public health concern. Monitoring road safety initiatives' efficacy necessitates analysing RTA incidence. This study examines time zone-specific RTA mortality in Kerala state, India, from 2016 to 2021. Methods: Utilizing compiled secondary-level time series data, the study encompasses total RTA fatalities in Kerala from 2016 to 2021. Data includes fatalities per year in nine consecutive three-hour time periods. Exploratory data analysis, time series regression, and exponential smoothing were employed for analysis. Results: Data reveals fluctuating trends in road accident (RA) fatalities, peaking in 2018 with a notable decrease in 2020. 18:00 to 21:00 recorded the highest and lowest fatalities, total 901 deaths. Disproportionate RA fatalities occurred from 06:00 to 09:00 (527 deaths) and 15:00 to 18:00 (697.5 deaths). The study employs Holt-Winters exponential smoothing for short-term forecasting, with a mean absolute scaled error (MASE) less than 1 signifying accurate predictions. Conclusions: The analysis highlights temporal patterns, emphasizing 18:00 to 21:00 as critical. Holt-Winters exponential smoothing proves vital for accurate short-term forecasting, with MASE reflecting precision. Urgency is stressed in adopting targeted measures for time-specific road accidents. Government intervention is pivotal, advocating for improved infrastructure, enhanced driver education, efficient vehicle management, and sustained traffic enforcement. Tailoring traffic laws to time zones, coupled with forecasting techniques, aligns with the overarching goal of enhancing road safety and reducing RA mortality rates.

Diffusion Covid-19 in polluted regions: Main role of wind energy for sustainable and health

The COVID-19 pandemic, caused by the coronavirus disease 2019, is rapidly spreading worldwide, resulting in a significant number of deaths. This ongoing environmental and sustainability discussion has raised novel and relatively unexplored questions. One of these questions pertains to the impact of heavily polluting industrial processes and less environmentally friendly production methods on the spread of COVID-19 infections. This study aims to elucidate the connection between air pollution, particulate emissions, wind conditions, energy production, and the dissemination of COVID-19 infections. The primary focus of the statistical analysis is on Italy, a country that has seen a rapid surge in confirmed cases and fatalities. The findings of this study can be summarized as follows: 1) Cities located in regions with high wind speeds and significant wind energy production in megawatts (MW) tend to have fewer COVID-19 infections. 2) Conversely, cities situated in hinterland areas, especially those adjoining large urban conglomerates, characterized by heavy industrialization, low wind speeds, and less environmentally friendly production methods, tend to have a higher number of COVID-19 cases and total fatalities. These results suggest that addressing the current COVID-19 pandemic and preparing for future epidemics similar to COVID-19 requires more than just advancements in medical and microbiological research and practices. It also necessitates a proactive approach focused on sustainable development. In conclusion, this study underscores the importance of incorporating sustainability science into strategies aimed at preventing future epidemics like COVID-19. Such an approach involves promoting renewable energy sources and cleaner production methods to reduce pollution from industrialization, ultimately influencing the factors that contribute to the transmission of coronavirus diseases and other infections within society.

Identifying Critical Factors and Trends Leading to Fatal Accidents in Small-Scale Construction Sites in Korea

Small-scale construction sites in South Korea account for about 91.5% of all construction workplaces and contribute to 72.3% of the total accidents and fatalities. Safety measures at these sites are often underestimated, and proper safety education is lacking. In particular, the fatality rate is about 4.43 times higher compared to medium-/large-scale construction sites. In this study, a systematic analysis was conducted to examine the causes and trends of industrial accidents in small-scale construction sites to address these issues. This study analyzed industrial accidents in small-scale construction sites using statistical analysis, LDA topic modeling, and network analysis based on data from the Korea Occupational Safety and Health Agency (KOSHA) from 2018 to 2022. The analysis revealed that the most critical cause of accidents in small-scale construction sites is ‘Scaffolding and working platforms’, with accidents primarily involving ‘Fall’. Furthermore, various risk factors and accident trends were identified in apartment construction, new building projects, and mobile scaffolding usage. This study systematically analyzed the causes and trends of industrial accidents at small-scale construction sites, providing important evidence to enhance safety management and preventive measures. The results are expected to play a crucial role in establishing a safety culture at construction sites and ensuring the wellbeing of construction workers.

Evaluation of vaccine rollout strategies for emerging infectious diseases: A model-based approach including protection attitudes

Vaccine allocation strategies become crucial during vaccine shortages, especially in the face of potential outbreaks of new infectious diseases, as witnessed during the COVID-19 pandemic. To address this, a specialized compartmental model is created, which simulates an emerging infectious disease similar to COVID-19. This model divides the population into different age groups and is used to compare various vaccine prioritisation approaches, aiming to minimize the total number of fatalities. The model is an improvement upon previous ones as it incorporates essential behavioural factors and is adapted to account for the protective effects of vaccination against both disease infection and transmission. It takes into account human behaviors such as mask-wearing and social distancing by utilizing specific parameters related to self-protection, awareness levels, and the frequency of daily person-to-person interactions within each age group. Furthermore, a novel method for dynamic vaccine prioritisation was introduced in this study. This approach is model-independent and relies on the dynamic R number. It is the first time such a method has been developed, offering a decision-making approach that is not tied to any specific model. This innovation provides a flexible and adaptable strategy for determining vaccine priorities based on real-time data and the current state of the outbreak.Our findings reveal crucial insights into vaccine allocation strategies. When the daily rollout rates are fast (0.75% or higher) and children are eligible for vaccination, prioritising groups with high daily person-to-person interactions can lead to substantial reductions in total fatalities (up to approximately 40% lower). On the other hand, if rollout rates are slower and overall vaccination coverage is high, focusing on vaccinating elders emerges as the most effective strategy, resulting in up to approximately 10% fewer fatalities. However, the scenario changes significantly when children are not eligible for vaccination, as they constitute a highly interactive population group. In this case, the differences between priority strategies become smaller. With fast daily rollout rates, prioritisation based on interactions achieves only a 7% reduction in total fatalities, while a slower rollout with vaccination of elders first leads to an approximately 11% reduction in fatalities compared to the scenario where children are eligible for vaccination. The impact of behavioural parameters is equally critical. When the self-protection levels exercised by the population are low, it significantly affects the optimal vaccine prioritisation strategy to be followed, making it essential to consider behavioural factors in decision-making.

Risk perceptions of pedestrians for traffic and road features

Traffic fatalities from 2015 to 2019 in Uttar Pradesh (UP), India show that pedestrians and cyclists have the largest share of total road fatalities. This study analyzed the pedestrian’s perceptions of risk in the medium-sized city-Bulandshahr-UP, India regarding the traffic and road features. Perception of risk provides important information in identifying potential risks and explaining travel choices by pedestrians. The study locations were selected based on identified blackspots i.e. clustering of actual fatal crashes during 2015–2019 in UP. The types of locations at the blackspots were intersections below flyover, four-way signalized intersections, midblocks and foot of flyovers. An empirical analysis is presented in the study by taking pedestrians’ ranking of the selected risk factors like traffic speed, free left turn at intersections, unmarked crosswalks, median width, traffic volume and the number of lanes and using the Rank-ordered logit model. Traffic speed and median width were ranked as the two highest risk factors by pedestrians. The results also indicated that increased numbers of lanes are more likely to be perceived riskier by older age groups of pedestrians and females at intersections below flyovers and midblocks. A comparison of different locations shows that all the factors were significant at four-way signalized intersections, indicating more perceived risk by pedestrians at intersections. These significant results can be used by practitioners to design safer intersections and midblocks at selected locations for pedestrians in UP, India.