Journal of Safety Science and Resilience
Articles in this journal
Journal of Safety Science and Resilience; 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; 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; doi:10.1016/j.jnlssr.2020.06.002
Journal of Safety Science and Resilience; 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; 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 is 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 is optimized using the Levenberg-Marquardt optimization training algorithm (LM) with the overall highest R2 values of 0.97.
Journal of Safety Science and Resilience; doi:10.1016/j.jnlssr.2020.06.006