Results in Journal Journal of Safety Science and Resilience: 20
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Journal of Safety Science and Resilience, Volume 1, pp 135-140; doi:10.1016/j.jnlssr.2020.11.005
COVID-19 epidemic is declared as the public health emergency of international concern by the World Health Organisation in the last week of March 2020. This disease originated from China in December 2019 has already caused havoc around the world, including India. The first case in India was reported on 30th January 2020, with the cases crossing 4 million on the day paper was written. This pandemic has caused more than 80,000 fatalities with 3 million recoveries. Strict lockdown of the nation for two months, immediate isolation of infected cases and app-based tracing of infected are some of the proactive steps taken by the authorities. For a better understanding of the evolution of COVID-19 in the world, study on evolution and growth of cases in India could not be avoided. To understand the same, one of the compartment model: Susceptible-Infectious-Quarantined-Recovered (SIQR) is used. Recovery rate and doubling rate of the total reported positive cases in the country had crossed 75% and 25 days, respectively. It is also estimated that there is a strong positive correlation between testing rate and detection of new cases up to 6 million tests per day. Using the SIQR modelling effective reproduction number, epidemic doubling rate and infected to quarantined ratio is determined to check the temporal evolution of the pandemic in the country. Effective reproduction number that was at its peak during first half of the April is gradually converging to 1. It is also estimated using this model that with each detected cases in India, there could be 10–50 undetected cases. Like every mathematical model, this model also has some assumptions. To make this model more robust, a technique with weighted parameter that can avoid a person with a strong immune system to be equally vulnerable to the infection, can be worked out. Machine learning algorithms can also be used to train our model with the data of other countries to make the analysis and prediction more precise and accurate.
Journal of Safety Science and Resilience, Volume 1, pp 70-72; doi:10.1016/j.jnlssr.2020.06.012
The crisis of coronavirus disease (COVID-19) is growing and has a potential impact on low-and-middle-income countries (LMICs), particularly in Asian regions, which, have reported less so far. The main purpose of this article is to discuss the challenges of COVID-19 management in Asian LMICs and propose some future guidelines to control the transmission and spread. Due to a highly dense population, low-resourced health system, lack of testing kit, and tracing, the ultimate impact could be lasting. It is highly likely that Asian LMICs were not in the center of receiving high priority by the international community in order to take effective prevention strategies. This might upsurge caseloads. Like the symptoms, mode of transmission varies largely due to a number of unknown clinical features of the virus, defining a risk group only based on some limited criteria such as age and disease with comorbidity could be risky. Recently some of the Asian LMICs launched contact tracing app. However, the study has identified several healthcare challenges in Asian LMICs such as shortage of supply of PPE, inadequate long-term care services, the socio-cultural differences which require utmost consideration. In addition, to control infection and enhance prevention, some pertinent problems need to be addressed. Based on that, some multifactorial approaches can be taken in line with international guidelines. Finally, this paper recommends developing a strong and resilient healthcare monitoring system.
Journal of Safety Science and Resilience, Volume 1, pp 73-79; doi:10.1016/j.jnlssr.2020.07.002
Recent years have seen increasing school safety events along with growing numbers of students in China. In this paper, more than 400 serious school safety events between 2000 and 2018 in China were collected. The causes and characteristics of these events, taking into account the occurrence years, months, regions, education stages and types, were investigated. The results indicate that the number of school safety events has generally increased annually from 2000 to 2018 and can significantly vary each month, showing a higher frequency of occurrence during the “First Semester” (generally from September to December in China). Moreover, spatial distribution of school safety events is normally related to regional economic development; it was found that Guangdong, Jiangsu and Shandong is a first-level occurrence hotspots, followed by Zhejiang, Henan, Hebei and Sichuan, which are secondary occurrence hotspots. Furthermore, statistical analysis shows that the number of school safety events that occurred in kindergartens, primary schools and middle schools are approximately equal (around 1/3). Finally, it was found that the school safety events caused by “Accident” (such as school bus accidents, school fires, crowded stampedes and the collapses of school buildings) occupy a large proportion (57%).
Journal of Safety Science and Resilience, Volume 1, pp 67-69; doi:10.1016/j.jnlssr.2020.06.002
Journal of Safety Science and Resilience, Volume 1, pp 80-90; doi:10.1016/j.jnlssr.2020.06.006
The Public Works Research Institute Distributed Hydrological (PWRI-DH) for flood modeling is a combination of the tank model and the kinematic wave method. In the PWRI-DH model, fitting the required parameters plays a fundamental role. The developers of the PWRI-DH model have introduced the capability of obtaining parameters automatically using the baseline parameters; however, the results are not always the expected results because they depend on several factors and must be calibrated manually. The last issue has limited the interest of researchers regarding in the usage of the PWRI-DH model. In this paper, we present a methodology to obtain the parameters required for the PWRI-DH model that enables to focusing only on the key parameters. First, a parametric study is performed by identifying the influence of each parameter in the discharge. From this study, we found that only four parameters play a fundamental role in the flood modeling using the PWRI-DH model. Five flood events in the Upper Aikawa River basin are used to calibrate the model. The results showed that the proposed methodology is suitable and improve the efficient on the flood simulation of Aikawa River and similar rivers, when using the PWRI-DH model.
Journal of Safety Science and Resilience, Volume 1, pp 91-96; doi:10.1016/j.jnlssr.2020.07.003
The COVID-19 was firstly reported in Wuhan, Hubei province, and it was brought to all over China by people travelling for Chinese New Year. The pandemic coronavirus with its catastrophic effects is now a global concern. Forecasting of COVID-19 spread has attracted a great attention for public health emergency. However, few researchers look into the relationship between dynamic transmission rate and preventable measures by authorities. In this paper, the SEIR (Susceptible Exposed Infectious Recovered) model is employed to investigate the spread of COVID-19. The epidemic spread is divided into two stages: before and after intervention. Before intervention, the transmission rate is assumed to be a constant since individual, community and government response has not taken into place. After intervention, the transmission rate is reduced dramatically due to the societal actions or measures to reduce and prevent the spread of disease. The transmission rate is assumed to follow an exponential function, and the removal rate is assumed to follow a power exponent function. The removal rate is increased with the evolution of the time. Using the real data, the model and parameters are optimized. The transmission rate without measure is calculated to be 0.033 and 0.030 for Hubei and outside Hubei province, respectively. After the model is established, the spread of COVID-19 in Hubei province, France and USA is predicted. From results, USA performs the worst according to the dynamic ratio. The model has provided a mathematical method to evaluate the effectiveness of the government response and can be used to forecast the spread of COVID-19 with better performance.
Journal of Safety Science and Resilience, Volume 1, pp 120-127; doi:10.1016/j.jnlssr.2020.08.002
Novel coronavirus, now named COVID-19, has swept the world, which is regarded as ‘public enemy number one’ by WHO. In these months, the coronavirus has become a hot topic and led various public opinion. The traditional strategies for public opinion analyzing seldom take the entities and behaviors into consideration. Focusing on the high fluctuation of public opinion of novel coronavirus event, we propose a Key-Information-oriented Convolutional Neural Network (KINCNN) to analyze both relevant entities and behaviors in addition to public opinion trend on Chinese corpus. Firstly, we establish a knowledge set according to the characteristic of distribution in corpus of emotions, behaviors and entities. Secondly, we integrate the other prior knowledge to initialize the convolution kernel for better model performance. Thirdly, as COVID-19 event develops, the dominant public opinion trend is obtained by our approach. Furthermore, the relationship of dominant public opinion with entities and behaviors is established as well in this research.
Journal of Safety Science and Resilience, Volume 1, pp 106-119; doi:10.1016/j.jnlssr.2020.09.001
In order to study the current situation and prospects of an improved safety climate, this paper provides an extensive literature review and analysis of the concept of safety climate. Following that, a summary of research results and a theoretical framework is presented. First, several definitions of safety climate are reviewed and discussed, and the significance of safety climate to the workshop is considered. Future potential focus areas in safety climate research are noted through the study of measurement tools and the relationship of safety climate to safety performance and safety behavior. Second, the structural model of the safety climate principle and its connotation explanation are also proposed. The five principles consist of 22 levels of safety principles, which can be combined into an overall safety climate structure. The literature on safety climate is drawn from accidents that clearly demonstrate the impact of safety climate on enterprise, government, and society. Finally, six new trends in safety climate research are highlighted. These include the relationship between a feeling of safety and the reality theory, the elimination of cognitive biases in risk perception, safety climate research from a psychological perspective, the quantitative development of safety climate research, the multidimensional structure of a safety climate and the influence mechanisms of safety climate on safety behavior. The results indicate that the safety climate is composed of four dimensions: (1) the attitude of senior executives, (2) safety supervision, (3) safety production environment and (4) the implementation of safety training and education. The aim of this in-depth analysis is to contribute to future research paths across several domains, suggesting the need for a multi-disciplinary approach.
Journal of Safety Science and Resilience, Volume 1, pp 97-105; doi:10.1016/j.jnlssr.2020.08.001
Fires at the Wildland-Urban Interface (WUI) are becoming increasingly hazardous for life safety and property protection. Guidelines and standards for fire practitioners are needed in order to help WUI communities face this threat and become fire-adapted. A performance-based design approach (PBD) is proposed to deal with the complex issues present at the WUI homeowner scale, which entails the use of Computational Fluid Dynamics (CFD) tools such as FDS in order to identify vulnerabilities in a quantitative manner. An analysis of recent European WUI fires is presented, along with the definition of several pattern scenarios that can be derived from these. Based on this analysis, examples of PBD fire scenarios specific for the Mediterranean WUI microscale are presented, involving glazing systems, roofing and gutters, external structures adjacent to the main building, and gaps present in the building envelope. A worked example to show the implementation of the proposed PBD method is provided in which the fire impact of residential fuel on a glazing system is quantitatively analysed.
Journal of Safety Science and Resilience, Volume 1, pp 128-134; doi:10.1016/j.jnlssr.2020.11.002
At the end of 2019, the novel coronavirus (COVID-19) outbreak was identified in Wuhan city of China. Because of its profoundly infectious nature, it was converted into a pandemic in a very short period. Globally, 6,291,764 COVID-19 confirmed cases, and a total of 374,359 deaths are reported as of 1st June 2020; nevertheless, the circumstances become more and more critical over time. Controlling this global pandemic has necessitated extensive strategies putting into practice. Based on the intensity of the epidemic, the model discriminates area into three different types of zones, and this distinction is crucial to construct various effective strategies for all three types of zones separately. The threshold value of the COVID-19 based on the data from zone-wise distribution of Indian districts from 15th April to 3rd May 2020 is calculated. Furthermore, the model is modified in a multi-group model to analyse the global transmission of COVID-19. Optimal control theory is applied to the model. Five control strategies are included based on the level of intensity of COVID-19 .
Journal of Safety Science and Resilience; doi:10.1016/j.jnlssr.2020.11.003
Emergency events need early detection, quick response, and accuracy recover. In the era of big data, the use of social media platforms is being popularized. Social media users can be seen as social sensors to monitor real time emergency events. In this paper, a similarity-based method is proposed to early detect all kinds of emergency events in social media, including natural disasters, accidents, public health events and social security events. The method focuses on clustering social media texts based on the 3W attribute information (What, When, and Where) of events. First, with the two-step classification, emergency related messages are detected and divided into different types from the massive and irrelevant data. Second, the time and location information are respectively extracted with the regular expression matching and the BiLSTM model. Finally, the text similarity is calculated using the type, time and location information, based on which social media texts are clustered into different events. The experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show the proposed framework has good performance and high timeliness. As the attribute information of events is extracted during the algorithm flow, it can be described what emergency, and when and where it happened.
Journal of Safety Science and Resilience, Volume 1, pp 44-56; doi:10.1016/j.jnlssr.2020.06.009
2019/20 Australia's bushfire season (Black Summer fires) occurred during a period of record breaking temperatures and extremely low rainfall. To understand the impact of these climatic values we conducted a preliminary analysis of the 2019/20 bushfire season and compared it with the fire seasons between March 2000 and March 2020 in the states of New South Wales (NSW), Victoria, and South Australia (SA). Forest and fire management in Australia were asked to provide data on the number of fires, burned area, life and house loss, as well as weather conditions. By March 2020 Black Summer fires burnt almost 19 million hectares, destroyed over 3,000 houses, and killed 33 people. Data showed that they were unprecedented in terms of impact on all areas. A number of mega-fires occurred in NSW resulting in more burned area than in any fire season during the last 20 years. One of them was the largest recorded forest fire in Australian history. Victoria had a season with the highest number of fires, area burned, and second highest numbers of houses lost for the same period. SA had the highest number of houses lost in the last 20 years. Black Summer fires confirmed existing trends of impact categories during the last two decades for NSW and Victoria. It showed that the smoke from the bushfires may be a significant concern in the future for the global community, as it travels to other countries and continents. Based on preliminary data, it will take many years to restore the economy and infrastructure in impacted areas, and to recover animal and vegetation biodiversity.
Journal of Safety Science and Resilience, Volume 1, pp 1-2; doi:10.1016/j.jnlssr.2020.06.001
Journal of Safety Science and Resilience, Volume 1, pp 57-58; doi:10.1016/j.jnlssr.2020.06.003
One of the ubiquitous human behaviours observed in natural disasters and humanitarian crisis is irrational stockpiling (also known as hoarding or panic buying). Limited, distorted and exaggerated information during crisis disturbs people's judgement and results in aberrant actions which can be explained with economics and psychology theories. The objective of this paper is to examine the perplexing stockpiling phenomena during disasters like COVID-19 pandemic and discuss its immediate and long-term impact on economy, society and local communities.
Journal of Safety Science and Resilience, Volume 1, pp 12-18; doi:10.1016/j.jnlssr.2020.06.007
In this paper, we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India. We adopted an Auto-Regressive Integrated Moving Average (ARIMA) model on the data collected from 31st January 2020 to 25th March 2020 and verified it using the data collected from 26th March 2020 to 04th April 2020. A nonlinear autoregressive (NAR) neural network was developed to compare the accuracy of predicted models. The model has been used for daily prediction of COVID-19 cases for next 50 days without any additional intervention. Statistics from various sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/ are used for the study. The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases with approximately 1500 cases per day, based on available data as on 04th April 2020. The appropriate ARIMA (1,1,0) model was selected based on the Bayesian Information Criteria (BIC) values and the overall highest R2 values of 0.95. The NAR model architecture constitutes ten neurons, which was optimized using the Levenberg-Marquardt optimization training algorithm (LM) with the overall highest R2 values of 0.97.
Journal of Safety Science and Resilience, Volume 1, pp 3-11; doi:10.1016/j.jnlssr.2020.06.004
The undefendable outbreak of novel coronavirus (SARS-COV-2) lead to a global health emergency due to its higher transmission rate and longer symptomatic duration, created a health surge in a short time. Since Nov 2019 the outbreak in China, the virus is spreading exponentially everywhere. The current study focuses on the relationship between environmental parameters and the growth rate of COVID-19. The statistical analysis suggests that the temperature changes retarded the growth rate and found that -6.28 °C and +14.51 °C temperature is the favorable range for COVID-19 growth. Gutenberg- Richter's relationship is used to estimate the mean daily rate of exceedance of confirmed cases concerning the change in temperature. Indeed, temperature is the most influential parameter that reduces the growth at the rate of 13–17 cases/day with a 1 °C rise in temperature.
Journal of Safety Science and Resilience, Volume 1, pp 19-30; doi:10.1016/j.jnlssr.2020.06.008
Resistance to unexpected disasters and rapid post-disaster recovery (i.e., disaster resilience) of cities are extremely necessary owing to the concentrated risk of urbanization. Resilience quantification can adequately reflect the capacity of a city to withstand disasters. Many existing studies have focused on and proposed several frameworks on the quantitative measures of disaster resilience, and the corresponding research objects include different types of disasters (e.g., earthquake, hurricane, flood, and fire), various domains (e.g., engineering, social, and economic), and multiple levels (e.g., city, community, and building). Among these research objects, studies on seismic resilience in civil engineering are relatively comprehensive. Specifically, studies on resilience in civil engineering have paid significant attention to the dynamics of engineering facilities and the engineering-related social and economic functions, including city-scale engineering, social, and economic functionalities, and essential functionalities of building, transportation, lifeline, and nonphysical subsystems of a city. Consequently, based on the review of resilience studies carried out in recent years, the framework and specifications for the quantification of disaster resilience of civil engineering systems subjected to earthquakes and other unexpected disasters are elaborated. Methods of disaster resilience assessment of cities and the corresponding subsystems are discussed. Furthermore, several case studies are reviewed, and resilience limit-state analyses of communities and buildings are performed.
Journal of Safety Science and Resilience, Volume 1, pp 59-65; doi:10.1016/j.jnlssr.2020.06.010
Post-incident studies provide direct and valuable information to further the scientific understanding of Wildland-Urban Interface (WUI) fires. Most post-incident studies involve data collection in the field (i.e. a “research field deployment”). In this review, technical reports of post-incident studies for WUI fire and other natural disasters were analyzed and professionals directly involved in WUI fire research field deployments were interviewed. The goal of this review is to provide a resource for future WUI studies regarding the development of safe and effective fieldwork procedures, the collection and integration of accurate and relevant data, and the establishment of practical lessons learned. Three main stages of WUI fire post-incident studies are identified and described in detail. Data collection methodologies, data attributes, logistical practices and lessons-learned were compiled from various past studies and are presented here in the context of application to WUI fire.
Journal of Safety Science and Resilience, Volume 1, pp 31-35; doi:10.1016/j.jnlssr.2020.06.011
With the quick spread of pandemic disease, many individuals have lost their lives across different parts of the world. So, the need for a novel approach or model to overcome the problem becomes a necessity. In this paper, a mechanism is proposed called DBCMS (Drone Based Covid-19 Medical Service) for the safety of medical employees who are prone to Covid-19 infection. The proposed mechanism can effectively improve the treatment process of Covid-19 patients. Drones are nowadays commonly used in the field of medical emergency situations. The proposed model in this paper uses drone service to reduce the risk of infection to the doctors or other medical staff, thereby preventing the disease spread. This paper further assumes that the primary step is to isolate people at their home instead of admitting them to the hospitals, also called a situation of lockdown or curfew. Thus, in this way, the spread can be significantly reduced across the globe if DBCMS approach is implemented at cluster level.
Journal of Safety Science and Resilience, Volume 1, pp 36-43; doi:10.1016/j.jnlssr.2020.06.005
Entity perception of ambiguous user comments is a critical problem of target identification for huge amount of public opinions. In this paper, a Two-Step-Matching method is proposed to identify the precise target entity from multiple entities mentioned. Firstly, potential entities are extracted by BiLSTM-CRF model and characteristic words by TF-IDF model from public comments. Secondly, the first matching is implemented between potential entities and an official business directory by Jaro–Winkler distance algorithm. Then, in order to find the precise one, an industry-characteristic dictionary is developed into the second matching process. The precise entity is identified according to the count of characteristic words matching to industry-characteristic dictionary. In addition, associated rate (global indicator) and accuracy rate (sample indicator) are defined for evaluation of matching accuracy. The results for three data sets of public opinions about major public health events show that the highest associated rate and accuracy rate arrive at 0.93 and 0.95, averagely enhanced by 32% and 30% above the case of using the first matching process alone. This framework provides the method to find the true target entity of really wanted expression from public opinions.