scholarly journals Exploring the Impact of COVID-19 on Social Life by Deep Learning

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 459
Author(s):  
Jose Antonio Jijon-Vorbeck ◽  
Isabel Segura-Bedmar
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Social Life ◽  
Data Science ◽  
Short Term Memory ◽  
Negative Impact ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Health Organization ◽  
The Impact

Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model.

scholarly journals A review of particulate matter emissions and impacts on human health: A focus on Canadian agricultural and rural emission sources

2018 ◽  
pp. 6.09-6.21 ◽  
Author(s):  
Jennifer Spencer ◽  
Bill Van Heyst
Keyword(s):  
Particulate Matter ◽  
Human Health ◽  
Negative Impact ◽  
Fine Particulate Matter ◽  
World Health ◽  
Health Impacts ◽  
Inorganic Aerosols ◽  
Particulate Matter Emissions ◽  
Health Organization ◽  
The Impact

Particulate matter (PM) has been documented in an increasing number of research studies as having a known or suspected negative impact on human health. The World Health Organization (WHO) estimates that 3.1 million deaths were caused by ambient fine particulate matter (PM2.5) in 2010. While many Canadian studies focus on health impacts from PM2.5, there is a gap with respect to rural sourced PM2.5 and health impacts in these areas. This paper reviews the impact PM2.5 has on Canadians’ health, investigates where PM2.5 data is being gathered, and outlines the sources of PM2.5 reported. Secondary inorganic aerosols that are formed in and around animal production facilities due to the higher prevalence of ammonia gas is of particular interest. The conclusion drawn is that the reporting and gathering of rural sourced PM2.5 data is lacking, leading to a gap in the data used to determine the impacts on Canadian human health.

scholarly journals CHALLANGES FACING THE EMERGING ECONOMY DURING COVID-19 THE CASE OF JORDAN

2021 ◽  
Vol 6 (2) ◽  
pp. 21-29
Author(s):  
Basem Al-Lozi ◽  
Sheren Hamed
Keyword(s):  
Negative Impact ◽  
Positive Impact ◽  
World Health ◽  
Unique Contribution ◽  
Policy Recommendations ◽  
Exploratory Research ◽  
Policy Makers ◽  
The Public ◽  
Health Organization ◽  
The Impact

Purpose: The purpose of this study was to explore the challenges of the Jordanian economy during COVID-19. The Jordanian economy may have to face different scenarios in his macro-environment. Specifically, the study focused on the impact COVID-19 on the Jordanian economy. Methodology: An exploratory research method was used to build three scenarios. The sample randomly selected from Jordanians in the capital of Jordan Amman. The study divided the sample to three groups and asking them three questions related to the expectations of the impact of COVID-19 on the Jordanian economy for the coming years. Results: The study findings revealed that the majority of respondents (55.2%) are optimistic that the COVID-19 will finish and Jordan economy will be booming. Unique contribution to theory, practice and policy: Recommendations were provided for Jordanian policy makers to deal scenarios. For example Jordan government and policy makers has to be pragmatic, and work toward lowering level of expectations among Jordanian economy to avoid the negative impact of COVID-19 on the economy. Cooperation between the public sector and private sector in implementing the instructions of the World Health Organization and the Ministry of Health to decrease the number of cases to open more sectors which will have a positive impact on the Jordanian economy.

scholarly journals Reviewing the Global Smartphone Industry Strategic Implication in Response to COVID-19 Situation

2021 ◽  
Vol 15 (14) ◽  
pp. 169
Author(s):  
Samsul Alam
Keyword(s):  
Negative Impact ◽  
World Health ◽  
Smart Device ◽  
Entertainment Media ◽  
Global Pandemic ◽  
Strategic Approach ◽  
Significant Negative Impact ◽  
Current Scenario ◽  
Health Organization ◽  
The Impact

<p>The global smartphone brands were progressing aggressively over time. A sudden unexpected turbulent situation known as a global pandemic declared by the World Health Organization (WHO) about a century later in the history of human civilization stops this progress. It makes the industry bound to fall behind. This study aims to review and analyze the impact of the present pandemic situation due to Coronavirus Disease 2019 (COVID-19) on the global smartphone industry. It shows its competitive scenarios focusing on smartphone demand and supply. Thus, the study suggest a strategic approach to combat this situation. It is done by reviewing the latest literature published explicitly in 2020. The findings of this study reveal a significant negative impact of COVID-19 on global smartphone brands, primarily especially in the big markets of this industry, namely China, India, USA, Europe. Conversely, it can also positively impact the industry, especially in some developing countries. The positivity is seen due to the expanded demand for smartphones in some sectors like education, business, and entertainment media shifted online, triggering the user’s need to purchase a new smart device. Lastly, based on the understanding of the current scenario, some strategic approaches are discussed, and appropriate solutions are given for the industry to cope up with the pandemic crisis and, at the same time, how to attain success. The strategic directions given at the end can be applied to the industry’s sustainability and growth.</p>

scholarly journals MASK DETECTION SYSTEM FOR COVID-19 SCENARIO USING COMPUTER VISION

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr. Prakash Prasad ◽  
Mukul Shende ◽  
Mayur Karemore ◽  
Lucky Khobragade ◽  
Amit Dravyakar ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Detection System ◽  
Face Mask ◽  
World Health ◽  
Public Places ◽  
Learning Module ◽  
Different Types ◽  
Health Organization ◽  
The Impact

The new pandemic of (Coronavirus Disease-2019) COVID-19 continues to spread worldwide. Every potential sector is experiencing a decline in growth. (World Health Organization) WHO suggests that Wearing Face Mask can reduce the impact of COVID-19. So, This Paper Proposed a system that controls the growth of COVID-19 by finding individuals who don't wear masks in populated areas like malls, markets where all public places are under surveillance with closed-circuit television cameras (CCTV). When a person without a mask is found, the corresponding authority is informed by the CCTV network. And it can calculate the number of people that do not wear the mask and emit an audible signal to inform the authority. A deep learning module is trained on a dataset composed of images of people wearing different types of masks and people without masks collected from various sources. It also contains some confusing images that help the model to achieve greater precision than other models. This model will use the dataset to build a COVID-19 face mask detector with computer vision using Computer Vision. This approach allowed extracting even the details from the pixels

scholarly journals Short-Term Prediction of PM2.5 Pollution with Deep Learning Methods

2020 ◽  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
World Health ◽  
Short Term ◽  
Learning Methods ◽  
Term Prediction ◽  
Pollutant Concentrations ◽  
Health Organization ◽  
Air Quality Monitoring Stations ◽  
Short Term Prediction

<p>Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Stations of the Ministry of Environment and Urbanization of Turkey were utilized to obtain the data. Specifically, meteorological and air pollution data were obtained from a monitoring station located in Keçiören District of Ankara. Several trials were conducted using different combinations of RNN, GRU and LSTM models. Pollutant concentrations and meteorological factors were integrated into the model as input parameters to predict PM2.5 concentration for 1,2 and 3 hours. Best results with R2 of 0.83, 0.7 and 0.63 for 1-, 2-, and 3-hour predictions, respectively, were obtained by using a combination of GRU and RNN models. The results of this study are promising for explaining the effect of different deep learning models on prediction performance.</p>

scholarly journals THE IMPACT OF CORONAVIRUS PANDEMIC LOCKDOWNS ON SPORTS ECONOMICS

2021 ◽  
Vol 6 (2) ◽  
pp. 4-11
Author(s):  
Devecioğlu Sebahattin
Keyword(s):  
World Health Organization ◽  
Social Life ◽  
Close Contact ◽  
World Health ◽  
Sports Economics ◽  
Recreational Facilities ◽  
Community Sport ◽  
The World ◽  
Health Organization ◽  
The Impact

Coronavirus named Coronavirus 2019-nCoV or (Covid-19) by the World Health Organization (WHO) has spread to all corners of the globe, disrupting many aspects of day-to-day life. The pandemic has led nations to implement stringent social distancing measures to prevent the spread of the virus; these include stay-athome advice, closures to schools and businesses and a pause to everyday social life to reduce close contact among people. Sport is no exception: livelihoods in the sector have been severely disrupted; gym, leisure, sport and recreational facilities have shut down; professional and community sport is suspended; and major events have been cancelled. Studies show that this has many economic and social repercussions, with the sports and tourism sectors effectively shutting down, and the strictest lockdown measures.

scholarly journals COVID-19 Future Predictions Using Machine Learning Algorithms

2021 ◽  
pp. 102-110
Author(s):  
R. Saradha Devi ◽  
Dr. J. G. R. Sathiaseelan
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Short Term Memory ◽  
Original Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Human Beings ◽  
The World ◽  
Health Organization ◽  
Deep Learning Neural Network

Corona Virus Infectious Disease (COVID-19) is an infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last year. COVID-19 was declared “Pandemic” by the World Health Organization (WHO) on March 11, 2020. This research proposed a method for confirming COVID-19 instances after doctors' diagnoses. The goal of this study is to see how similar the projected findings are to the original data in COVID-19 Confirmed-Negative-Released-Death situations using machine learning. This paper suggests a verification approach created on the Deep-learning Neural Network concept for this purpose. Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also used in this framework to train the dataset. The outcomes of the forecast match those predicted by clinical doctors.

scholarly journals Medical and social features of covid-19 in ukraine

2021 ◽  
Vol 9 (6) ◽  
pp. 219-224
Author(s):  
O.G. Shekera ◽  
V.I. Tkachenko
Keyword(s):  
Negative Impact ◽  
Clinical Manifestations ◽  
World Health ◽  
Social Significance ◽  
Respiratory Problems ◽  
Object A ◽  
Literary Sources ◽  
Significant Negative Impact ◽  
Health Organization ◽  
The Impact

Background. The COVID-19 pandemic, caused by the SARS-CoV-2 coronavirus, was announced by the World Health Organization on March 11, 2020 and identified as having a significant negative impact on the functioning of health systems and the economic development of countries. To date, we know the impact of the pandemic on the decrease in the availability of medical care, difficulties with the annual monitoring and screening of socially significant diseases. But we will be able to fully assess the medical, clinical and psychosocial consequences of the pandemic, which are just beginning to manifest themselves, only in the coming years. The purpose was to study and analyze the features of clinical manifestations, diagnosis, treatment, immunity and social significance of the COVID-19 pandemic in Ukraine. Materials methods. The available current normative and literary sources were investigated. Methods: bibliosemantic - for studying the lokal and world science and information space on the problem under study; a systematic approach and analysis - for a comprehensive study of an object, a subject in a systemic relationship, for analyzing problematic issues, ways to solve them. Results. The medical feature of COVID-19 in Ukraine is the two-stage course of the disease 1-10 days - active replication of the virus; 8-14 days or longer - immune dysfunction (eg, respiratory problems, other serious conditions). Often recorded injury of the lungs in the form of pneumonitis, although it is mainly the disease of mild or moderate severity. Diagnostic is based on the detection of the RNA of the virus using PCR or immunoglobulins by ELISA. Treatment is mostly symptomatic. The social significance of the COVID-19 pandemic was reflected in the increase in patients with mental disorders, victims of domestic violence, deterioration in the health of the population due to the limited availability of many types of medical services, increased workload and housework, decreased income and a deterioration in the quality of life. Conclusions. The COVID-19 pandemic over the year of its existence has caused changes in many spheres of life of the world's population, and Ukraine is not an exception. Disease COVID-19 has nonspecific symptoms, can proceed under the mask of many pathological conditions. especially in the presence of comorbid diseases. The pathogenetic features of the course of COVID-19 are poorly understood, which limits the possibilities of effective etiopathogenetic therapy. The virus constantly mutates and leaves an unstable and short-lived immunity, which explains its diversity of the clinical symproms in different populations. In addition to a direct increase in morbidity and mortality due to COVID-19, the virus can affect the psychoemotional state of people, reduce income and deterioration in nutrition, especially among the most vulnerable segments of the population, which requires taking these aspects into account when making decisions at the legislative level with an emphasis on addressing gender and social inequality.

scholarly journals COVID-19 Pandemic: Oral Health Challenges and Recommendations

2020 ◽  
Vol 14 (S 01) ◽  
pp. S165-S170
Author(s):  
Fathima Fazrina Farook ◽  
Mohamed Nizam Mohamed Nuzaim ◽  
Khansa Taha Ababneh ◽  
Abdulsalam Alshammari ◽  
Lubna Alkadi
Keyword(s):  
Oral Health ◽  
Disease Transmission ◽  
Negative Impact ◽  
Dental Health ◽  
Transmission Risk ◽  
World Health ◽  
Dental Procedures ◽  
Control And Prevention ◽  
Health Organization ◽  
The Impact

AbstractThe aim of this article is to shed light on coronavirus disease 2019 (COVID-19) and its oral effects and risk of nosocomial transmission to update the knowledge of dental health care workers. A thorough literature search of the PubMed/Embase/Web of Science/Cochrane central database was conducted to identify the impact of COVID-19 on oral health. We reviewed the recommendations on the recent guidelines set by the Centers for Disease Control and Prevention infection control practices for dentistry, American Dental Association, and the World Health Organization. According to the available evidence, COVID-19 may have a negative impact on the oral health due to the infection itself and due to various other consequences such as therapeutic measures, xerostomia, and other complications of the COVID-19. In light of the above facts, dentists should be wary of the disease, its identification, mode of spread and impacts on the oral health. The dental personnel have been identified as at the highest risk of getting COVID-19 due to cross infection from contact with their patients and aerosols generated in routine dental procedures. As such, they should be aware of the modifications that need to be made to the practice to prevent transmission of the disease. It is evident that COVID-19 has a negative impact on the oral health and at the same time a significant transmission risk to the dental personnel and patients who visit the clinic. If the recommendations issued by the regulatory authorities are meticulously followed, the risk of disease transmission can be lessened.

scholarly journals Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3089 ◽  
Author(s):  
Ayan Chatterjee ◽  
Martin W. Gerdes ◽  
Santiago G. Martinez
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Data Science ◽  
Short Term Memory ◽  
Transmission Rate ◽  
International Classification Of Diseases ◽  
World Health ◽  
External Temperature ◽  
Timeseries Analysis ◽  
Novel Coronavirus ◽  
Health Organization

“Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)”, the novel coronavirus, is responsible for the ongoing worldwide pandemic. “World Health Organization (WHO)” assigned an “International Classification of Diseases (ICD)” code—“COVID-19”-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with “Middle East Respiratory Syndrome (MERS-CoV)”, “Severe Acute Respiratory Syndrome (SARS-CoV)”, and now with this new virus, namely “SARS-CoV-2”, or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a “spread factor (f)”, and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in “ourworldindata.org (Oxford University database)”, and newly simulated datasets, following the analysis of different univariate “Long Short Term Memory (LSTM)” models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months—January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.

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