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This chapter provides a summary of recent views on the aspects of vitamin D levels and the relationship between the prevalence rates of vitamin D deficiency and COVID-19 death toll of several countries in Europe and Asia. The chapter also discusses a new modified time-delay immune system model with time-dependent of the body's immune healthy cells, vitamin D, and probiotic. The model can be used to assess the timely progression of healthy immune cells with the effects of the levels of vitamin D and probiotics supplement. It also can help to predict when the infected cells and virus particles free state can ever be reached as time progresses with and without considering the vitamin D and probiotic supplements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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In the past few years, HIV, SARS, cryptococcal meningoencephalitis, and COVID-19 have been worsening. The world is exterminated by pandemic COVID-19, causing tremendous death tolls, economic chaos, and social disruptions. Since the COVID-19 pandemic, the wildlife trade has been seriously re-evaluated. Twitter, as a social media platform, can be a challenging place to collect data in the form of tweets that are currently attracting the attention of many people. Nevertheless, human beings find it relatively difficult to extract latent information from a set of texts to generate particular topics. The process of evaluating the topic model started with understanding its importance. As a next step, we reviewed existing methods for topic coherence, along with the available measures of topic coherence. In order to establish a baseline coherence score, we used Gensim to implement a default Latent Dirichlet Allocation (LDA) model and discuss ways to optimize the LDA hyperparameters. © 2022 IEEE.
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The COVID-19 epidemic continues to have a negative impact on the economy and public health. There is a correlation between certain limits (meteorological factors and air pollution statistics) and verified fatal instances of Corona Virus Disease 2019 (COVID-19), according to several researchers. It has not yet been determined how these elements affect COVID-19. Using air pollution data and meteorological data from 15 cities in India from 2020 to 2022, Convergent Cross Mapping (CCM) is utilized to set up the causal link with new confirmed and fatal cases of COVID-19 in this study. Our experimental results show that the causal order of the factors influencing the diagnosis of COVID-19 is: humidity, PM25, temperature, CO, NO2, O3, PM10. In contrast to other parameters, temperature, PM25, and humidity are more causally associated with COVID-19, while data on air pollution are less causally related to the number of new COVID-19 cases. The causal order of the factors affecting the new death toll is as follows: temperature, PM25, humidity, O3, CO, PM10, NO2. The causality of temperature with new COVID-19 fatalities in India was higher than the causation of humidity with new COVID-19 deaths, and O3 also showed higher causality with it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The outbreak of the new crown pneumonia on a global scale has caused serious damage to every country, whether financial or human, and the death toll has also increased significantly. This enhances the importance of laboratory biosafety management, which is related to human life safety and should not be underestimated. In the past, biosafety has not received much attention, resulting in the biosafety management status of biosafety laboratories in my country is not optimistic. On the one hand, it is necessary to cope with reducing the number of people infected with pneumonia. On the other hand, there is an urgent need to match drugs against the new crown. Time is very short, and the number of infected people will increase rapidly according to time. Since viral nucleic acid testing is toxic, it has been reported in our country that many doctors have been infected with the new coronavirus, which has dealt a heavy blow to laboratory biosafety and the people of the country. Immediately afterwards, many medical universities were also exposed to lax safety management, lack of laboratory safety principles, and low safety factor for teachers and students. With the development of life safety and biotechnology, efficient implementation should strengthen research on biotechnology and management safety. The state also hopes that they can make rectifications. This also gives great trust to the management of biosafety laboratories in colleges and universities. I hope they can do their best. correct. © 2022 SPIE.
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It is evident that increasing the intensive-care-unit (ICU) capacity and giving priority to admitting and treating patients will reduce the number of COVID-19 deaths, but the quantitative assessment of these measures has remained inadequate. We develop a comprehensive, non-Markovian state transition model, which is validated through the accurate prediction of the daily death toll for two epicenters: Wuhan, China and Lombardy, Italy. The model enables prediction of COVID-19 deaths in various scenarios. For example, if appropriate treatment priorities had been used, the death toll in Wuhan and Lombardy would have been reduced by about 10% and 7%, respectively. The strategy depends on the epidemic scale and is more effective in countries with a younger population structure. Analyses of data from China, South Korea, Italy, and Spain suggest that countries with less per capita ICU medical resources should implement this strategy in the early stage of the pandemic to reduce mortalities. We emphasize that the results of this paper should be interpreted purely from a scientific and a quantitative-analysis point of view. No ethical implications are intended and meaningful. © 2022 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
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Lung diseases affect many populations around the world and their symptoms may range from common cough to chronic lung infections caused by unhygienic living conditions, unhealthy habits(smoking) and often inter-species virus/bacterial transmission. Moreover, the death toll and individuals affected by lung infections have skyrocketed after the contagious COVID-19 outbreak in 2019 December in Wuhan China. The Big Data revolution has increased the number of labelled and analyzed x-ray image data in the medical field, which has triggered more solutions for preventive and early diagnostics measures in the area. However contagious nature of COVID-19 makes it unsafe for medical practitioners despite the use of preventive gear and the varying examination skills of radiologists generates a biased result with different x-rays. Employing Deep Neural Network-based methodologies would help overcome the current issue. In this paper, we have compared the performance of pre-trained models Resnet18, Resnet50 and the fusion of the two Resnet models using transfer learning. We have performed cross-validation of 5 folds with 25 epochs for each fold to obtain the optimal metrics performance for all three models. Average accuracy, precision, f1-score and recall of 88.75%, 89.89%, 88.75% and 88.66% was reported for resnet18 respectively while Resnet50 yield 90.25%, 90.26%, 90.25% and 90.24% for the same. The proposed fusion model gave increased performance metrics with an accuracy of 95.75%, precision of 95.89%, recall of 95.75% and an f-1 score of 95.75%. © 2022 IEEE.
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The Covid-19 crisis has led to widespread impacts on society, not only in terms of high death tolls, but also in terms of cascading effects for essentially all societal sectors;especially due to all measures taken to reduce the spread of the disease. A range of actors have become involved in the response to the pandemic and to ensure appropriate actions being taken, these actors need good situational awareness, which can be facilitated by developing and distributing common operational pictures (COPs). This paper presents a case study of the COPs compiled by the Swedish Counties’ Coordination Office (SCCO). A point of departure for the paper is that for a COP to become useful, they should not only contain information about the present but also contain description about future potential states and uncertainties. Since this is a perspective that is addressed by the risk science, this paper addresses the question of how risk science can add value to the work with COPs in general and the SCCO’s COPs in particular. Our results indicate that SCCO’s COPs fulfil the expectations of the Government Office who are the main target organization. But there is room for improvements, where risk science can add value, if the aim is to improve their usefulness as decision basis. © ESREL 2021. Published by Research Publishing, Singapore.
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In the context of COVID-19 pandemic, social networks such as Facebook, Twitter, YouTube and Instagram stand out as important sources of information. Among those, YouTube, as the largest and most engaging online media consumption platform, has a large influence in the spread of information and misinformation, which makes it important to study how the platform deals with the problems that arise from disinformation, as well as how its users interact with different types of content. Considering that United States (USA) and Brazil (BR) are two countries with the highest COVID-19 death tolls, we asked the following question: What are the nuances of vaccination campaigns in the two countries? With that in mind, we engage in a comparative analysis of pro and anti-vaccine movements on YouTube. We also investigate the role of YouTube in countering online vaccine misinformation in USA and BR. For this means, we monitored the removal of vaccine related content on the platform and also applied various techniques to analyze the differences in discourse and engagement in pro and anti-vaccine "comment sections". We found that American anti-vaccine content tend to lead to considerably more toxic and negative discussion than their pro-vaccine counterparts while also leading to 18% higher user-user engagement, while Brazilian anti-vaccine content was significantly less engaging. We also found that pro-vaccine and anti-vaccine discourses are considerably different as the former is associated with conspiracy theories (e.g. ccp), misinformation and alternative medicine (e.g. hydroxychloroquine), while the latter is associated with protective measures. Finally, it was observed that YouTube content removals are still insufficient, with only approximately 16% of the anti-vaccine content being removed by the end of the studied period, with the United States registering the highest percentage of removed anti-vaccine content(34%) and Brazil registering the lowest(9.8%). © 2022 ACM.
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The COVID-19 pandemic has been one of the most highly discussed topics throughout its entire lifespan. This pandemic has caused huge losses throughout the world, ranging from economic losses to a massive death toll of over five million that is still growing. One aspect has not been touched on nearly as much as the impact on mental health worldwide. We decided to study the pandemic's impact on peoples' mental health, specifically depression and the many symptoms that come with it. Twitter was used for this study as it has many raw and unfiltered personal sentiments from its many users. Twitter also has a helpful developer API that allows tweets to be queried at a massive scale. Throughout our research, we found that the number of people discussing COVID-19 depression was much higher during the first six months of the pandemic. However, during the following six months, those still suffering from such depression were experiencing worse symptoms with greater frequency. © 2022 ACM.
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With the emergence of the COVID-19 pandemic, the demand for health services has exponentially increased, which caused the saturation of hospital beds and a high death toll. Motivated by the need to provide more agility in patients' attendance and unburden the health services, this work proposes a solution for automatic attendance via a Recommender System that uses sentence embeddings of text messages to train an LSTM classifier. This classifier can provide recommendations of a course of action for patients, instructing them to stay at home or seek medical support. Our numerical results validate the proposed solution and corroborate its reasonable accuracy rate. © 2021 ACM.
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Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is the main cause of Corona virus disease 2019 (COVID-19) resulting in a massive death toll across the world. In December 2019, Wuhan Province of China witnessed the first case of COVID-19 and within less time complete world suffered from this deadly virus. Medical imaging modalities like X-ray, Computed Tomography (CT), Medical Resonance Image (MRI) etc. plays vital role in detecting COVID-19. Further medical imaging when combined with the recently emerging technologies - Artificial Intelligence (AI), Deep Learning and Machine Learning (ML) strengthens the power of the imaging tools and help medical specialists for diagnosis. Moreover, the Computer Aided Diagnosis (CAD) platforms can also be developed to help radiologists make clinical decisions. This paper can provide the researchers and organizations with new insights in how the medical imaging along with recent technologies can aid to overcome the situation of COVID-19 by detecting and diagnosing in its early stage. © 2022 IEEE.
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In December 2019, an outbreak of a series of severe respiratory illness was found in Wuhan, Hubei Province, China. It was due to a novel coronavirus, now identified as SARS-CoV-2. The virus is human-to-human transmissible and that is why it has created a pandemic. Due to the continuous increasing death toll, several governments have been compelled to execute complete lockdown throughout the countries and followed by a social separation. The lack of tailored treatment remains an issue. Usually, patients above the age of 65 are more vulnerable to serious illnesses, according to epidemiological studies, whereas children have lesser symptoms. Depending on the present scenario of coronavirus disease, World Health Organization (WHO) advised to implement precautionary measures to defend self and other population. It has been also instructed to take legal action if some careless personnel do not abide with the guidelines of WHO and respective government of his country. A covid detection mechanism from X-ray images is presented in this paper, where a deep convolutional neural network has been utilized to determine whether a person is a covid patient or not. The proposed model accomplishes more than 96% accuracy, which proofs the goodness of the proposed work. © 2021 IEEE.
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Nowadays, freight transport is crucial in the functioning of cities worldwide. To dig further into the understanding of urban freight transport movements, in this research, we conducted a case study in which we estimated an origin-destination matrix for the trucks traveling on Autopista Central, one of Santiago de Chile’s most important urban highways. To do so, we used full real-world vehicle-by-vehicle information of freight vehicles’ movements along the highway. This data was collected from several toll collection gates equipped with free-flow and automatic vehicle identification technology. However, this data did not include any vehicle information before or after using the highway. To estimate the origins and destinations of these trips, we proposed a multisource methodology that used GPS information provided by SimpliRoute, a Chilean routing company. Nevertheless, this GPS data involved only a small subset of trucks that used the highway. In order to reduce the bias, we built a decision tree model for estimating the trips’ origin, whose input data was complemented by other public databases. Furthermore, we computed trip destinations using proportionality factors obtained from SimpliRoute data. Our results showed that most of the estimated origins belonged to outskirt municipalities, while the estimated destinations were mainly located in the downtown area. Our findings might help improve freight transport comprehension in the city, enabling the implementation of focused transport policies and investments to help mitigate negative externalities, such as congestion and pollution.
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The COVID-19 pandemic has jolted India as it has with the world, and the death toll has crossed the 1.5 lakh mark as of February 2021. To curb this wildfire like the spread of the virus, the Government of India has imposed a nationwide phased lockdown from 25 March 2020 to 31 May 2020. Studies have shown that more than 22 cities in India recorded a drastic decrease in PM2.5 during this lockdown period. This research aims to study the impact of this phased lockdown on Particulate Matter (PM2.5) by means of statistical analyses. The PM2.5 concentration for Pre-COVID years up to the end of Phase-IV of the lockdown is acquired via the continuous air quality monitoring stations of the Central Pollution Control Board in three locations;namely, Velachery, Alandur and Manali. Graphical analysis provides insight into the efficiency of lockdown showing April 2020 achieved the highest reduction in PM2.5 concentration in all three locations. Manali being an industrial area notices a significant increase as evidenced by the one-way ANVOA in May 2020 when the Government sanctioned relaxations on the logistical and industrial front. Analysis of Summer 2020 PM2.5 levels with previous years shows an overall decrease through the years and a significant decrease specifically in 2020. Comparison of air quality during the lockdown period with the previous years provides a distinctive perspective to understand the extent of anthropogenic influence on the air quality of Chennai, which can in turn act as a tool to identify suitable mitigation measures to vastly improve quality of life. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Purpose: Telecommuting can reduce traffic congestion, energy consumption, prevalence and a death toll of COVID-19 among employees due to less transportation and fewer physical contacts among employees, on the one hand, and efficiently develop their use of information and communications technology, on the other hand. In this regard, the present study aims to explore antecedents and consequences of telecommuting in public organizations. Design/methodology/approach: The study used a descriptive survey method to collect data. The statistical population includes all employees of government organizations in West Azerbaijan province in 2020, which according to the collected information, their number is equal to 63,079 employees. Based on Cochran's formula, a sample size of 686 people was obtained;stratified random sampling was used to select sampling. The process of calculating the sample volume was such that after referring to the preliminary sample and processing the collected data, the variance of the given answers was approximately 0.446. After obtaining the variance of the data, assuming a maximum acceptable error of 5% and a significance level of 0.05, the Cochran's formula calculated the sample size to be 686 people. In order to collect and measure data for the study, a standard questionnaire and the collected data were analyzed using structural equation modeling. Findings: Findings indicate that there is no meaningful relationship between the employees' physical job conditions or the quality of their life with telecommuting and that telecommuting does not have a significant effect on their life. However, job burnout, training and telecommuting experience have a significant positive effect on telecommuting, which in turn has a positive and significant effect on job security, job flexibility, organizational performance and overall productivity of employees. Research limitations/implications: This research is a cross-sectional study, and its data have been collected in a certain period of time, while longitudinal research can provide a richer result. Future research can benefit from the impact of employee isolation and telecommuter organizational commitment. Originality/value: This study hopes to contribute to the increase of the scientific knowledge in the telecommuting field and to allow organizations to rethink the telecommuting strategies to optimize resources and costs and to improve the organization's productivity without harming the quality of life and well-being of their workers. © 2021, Emerald Publishing Limited.
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During the pandemic of Corona-virus Disease 2019 (COVID-19), the whole world was confronted by a particularly high death toll and infection rate. Research has shown that air pollution plays a considerable part in the spread of certain illnesses and diseases. In the case of the COVID-19 pandemic, research has shown that increased air pollution has a negative effect on people's well-being and plays a role in the quick spread of the disease. Air pollution by itself affects the respiratory system of individuals which is aggravated, in addition, by a COVID19 infection. Some efforts have been made to use emerging technologies to combat the virus and its subsequent aerosol aspects to reduce transmission. In this context, we present an IoT system for Air Quality (AQ) monitoring and prediction using deep learning for data analysis and Augmented Reality (AR) for data visualization. The proposed system shows great potential for using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) units as a framework for leveraging knowledge from time-series data of AQ. Moreover, integrating AR visualization for the proposed IoT system enables intuitive interaction between users and IoT devices and further improves visualization of AQ data which effectively contributes to easily conducting a deeper analysis of data and makes faster decisions. © 2021 IEEE.
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This year proved to be a challenging one as the result of the coronavirus pandemic, an economic slump and social justice protests. For political scientists associated with the New England Political Science Association, we unfortunately did not have our annual conference as it was supposed to take place in Mystic, Connecticut. Since 2018, I have been fortunate to represent politics in the "Constitution State" along with other New England professors representing their respective states at the Clyde McKee Memorial New England Politics Roundtable. So this piece offers an analysis of what took place in Connecticut's General Assembly session as well as political concerns for the near future. At the beginning of the year, 2020 appeared to be just another legislative session for Connecticut's General Assembly. Among the various proposals and hearings planned, highway tolls and child immunization drew significant attention. The state's legislative body has a troubling habit of delaying controversial initiatives as well as drawing more attention to problematic concerns than it should.