This study presents the result of a traffic simulation analysis based on Floating Car Data and a noise emission assessment to show the impact of mobility restriction for COVID-19 containment on urban ...vehicular traffic and road noise pollution on the road network of Rome, Italy. The adoption of strong and severe measures to contain the spreading of Coronavirus during March-April 2020 generated a significant reduction in private vehicle trips in the city of Rome (-64.6% during the lockdown). Traffic volumes, obtained through a simulation approach, were used as input parameters for a noise emission assessment conducted using the CNOSSOS-EU method, and an overall noise emissions reduction on the entire road network was found, even if its extent varied between road types.
Background. The epidemic outbreak caused by coronavirus COVID-19 is of great interest to researches because of the high rate of the infection spread and the significant number of fatalities. A ...detailed scientific analysis of the phenomenon is yet to come, but the public is already interested in the questions of the epidemic duration, the expected number of patients and deaths. Long-time predictions require complicated mathematical models that need a lot of effort to identify and calculate unknown parameters. This article will present some preliminary estimates. Objective. Since the long-time data are available only for mainland China, we will try to predict the epidemic characteristics only in this area. We will estimate some of the epidemic characteristics and present the dependencies for victim numbers, infected and removed persons versus time. Methods. In this study we use the known SIR model for the dynamics of an epidemic, the known exact solution of the linear differential equations and statistical approach developed before for investigation of the children disease, which occurred in Chernivtsi (Ukraine) in 1988–1989. Results. The optimal values of the SIR model parameters were identified with the use of statistical approach. The numbers of infected, susceptible and removed persons versus time were predicted and compared with the new data obtained after February 10, 2020, when the calculations were completed. Conclusions. The simple mathematical model was used to predict the characteristics of the epidemic caused by coronavirus in mainland China. Unfortunately, the number of coronavirus victims is expected to be much higher than that predicted on February 10, 2020, since 12289 new cases (not previously included in official counts) have been added two days later. Further research should focus on updating the predictions with the use of up-to-date data and using more complicated mathematical models.
Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved ...vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images, respectively. Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused. Parallel architecture, which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people, was considered. The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%, sensitivity of 99.90%, specificity of 100%, precision of 100%, F1-score of 99.93%, MSE of 0.021%, and RMSE of 0.016% in a large-scale dataset. This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.
This study presents the results of a traffic simulation analysis and emissions (greenhouse gas and noise) assessment comparing pre-pandemic (2019) and post-pandemic (2022) periods. The estimation of ...road traffic demand is based on conventional data sources and floating car data; next, the traffic simulation procedure was performed providing road network traffic volumes, which are the input for the emission models. The diffusion of teleworking, e-commerce, as well as the digitization of many processes, services and activities, lead to a significant change in urban mobility. Results show a significant though still not complete resumption of commuters travel activity (−10% compared to pre-pandemic period) in the morning peak-hour. This translates into an 11% reduction of greenhouse gas emissions and a 0.1% increase in noise emissions.
Objective: Stressful events increase in traumatic conditions. Coronavirus is a new and serious challenge and significant public health problem, which can cause different stressors. This study has ...identified stressful events experienced by Iranian adults during the COVID-19 epidemic.
Method: Data on stressful events during the COVID-19 epidemic were collected online from 418 adults (mean age 37.16 years; 57.4% female and 42.6% male) using quota sampling method. Epidemic Stressful Events Checklist was applied for data collection. Data were analyzed by applying descriptive graphs and tables, the independent sample t-test, the Fisher’s F test, and post hoc Bonferroni test.
Results: The most frequent stressful event was rise in essential goods prices (84.7%); however, its perceived stress was not at the highest level. The highest severity of perceived stress was related to the death of a family member (4.83) due to COVID-19 infection, which was an event with the least occurrence, and the lowest severity of perceived stress was related to medical team performance (2.50). The results showed the severity of perceived stress is higher in women than men (t = 3.42; P value < 0.01) and also in the laboring occupations compared to other occupations (F = 3.18; P value < 0.05).
Conclusion: Traumatic events can lead to more serious concerns, eg., worrying about those we love, concerns about the future of our life, and about what politicians and macro planners will do to protect our lives. Moreover, traumatic events can cause concerns about food, basic needs, and lack of resources to survive.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
In this article, the author shares his eastern and western perspectives on biomedicine, science and arts as well as his personal experiences during the coronavirus epidemics in China. Virus is ...described symbolically as a messenger on the edge of art and science, human beings and nature leading to a discussion over the sustainable development of human beings in harmony with nature.
Every now and then, there has been natural or man‐made calamities. Such adversities instigate various institutions to find solutions for them. The current study attempts to explore the disaster ...caused by the micro enemy called coronavirus for the past few months and aims at finding the solution for the system of nonlinear ordinary differential equations to which q−homotopy analysis transform method (q−HATM) has been applied to arrive at effective results. In this paper, there are eight nonlinear ordinary differential equations considered and to solve them the advanced fractional operator Atangana‐Baleanu (AB) fractional derivative has been applied to produce better understanding. The outcomes have been presented in terms of plots. Ultimately, the present study assists in examining the real‐world models and aids in predicting their behavior corresponding to the parameters considered in the models.
With the global spread of the Coronavirus epidemic, search engine data can be a practical tool for decision-makers to understand the epidemic's trends. This article uses trend analysis data from the ...Baidu search engine, the most widely used in China, to analyze the public's attention to the epidemic and the demand for N95 masks and other anti-epidemic materials and information. This kind of analysis has become an important part of information epidemiology. We have analyzed the use of the keywords "Coronavirus epidemic," "N95 mask," and "Wuhan epidemic" to judge whether the introduction of real-time search data has improved the efficiency of the Coronavirus epidemic prediction model. In general, the introduction of the Baidu index, whether in-sample or out-of-sample, significantly improves the prediction efficiency of the model.
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