COVID-19 SEIAR Model with Sensitivity Analysis

Beside the unexpected toll of mortality and morbidity caused by COVID-19 worldwide, low- and middle-income countries are more suffering from the devastating issues on economic and social life. This disease has fostered mathematical modelling. In this paper, a SEIAR mathematical model is presented to illustrate how policymakers may apply efficient strategies to end or at least to control the devastating wide spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Engaging Online Learning at AAU and Its Impact on Students' Performance during COVID-19

The sudden and wide spread of the deadly severe acute respiratory syndrome coronavirus SARS-COV-2 (COVID-19) has disrupted the normal world we know. This pandemic has produced significant challenges on all world sectors including the global higher education community. In this paper, we present the effect of the COVID-19 pandemic on AL Ain University (AAU), analyse AAU response strategy to shift to an emergency remote, on-line, learning system and compare it with other universities' responses. The technological infrastructure readiness of AAU and how it shifted easily to online learning is discussed. A comparison between the results of some courses that were taught in-class previously (2019) against the ones that were taught remotely (2020) is presented. For the selected sample, results show that online teaching has a good impact on students' performance for many reasons such as saving traveling time, staying at home, and quarantine that imposes focusing on the study since other outdoor entertainments are closed. © 2022 IEEE.

Inorganic-based Surface Materials with Anti-SARS-CoV-2 Properties and Their Mechanisms of Action

The COVID-19 outbreak caused by SARS-CoV-2 has posed a serious threat to human health. The wide⁃ spread of the virus has increased the demand for anti-virus surface materials，especially in public places. This article reviews a series of inorganic surface materials with antiviral properties，including metals and its derivatives，graphene and its derivatives，and zeolites，and their antiviral mechanisms. The challenges and development prospects are summarized and prospected. © 2022 Higher Education Press. All rights reserved.

Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF

The sharing of fake news and conspiracy theories on social media has wide-spread negative effects. By designing and applying different machine learning models, researchers have made progress in detecting fake news from text. However, existing research places a heavy emphasis on general, common-sense fake news, while in reality fake news often involves rapidly changing topics and domain-specific vocabulary. In this paper, we present our methods and results for three fake news detection tasks at MediaEval benchmark 2021 that specifically involve COVID-19 related topics. We experiment with a group of text-based models including Support Vector Machines, Random Forest, BERT, and RoBERTa. We find that a pre-trained transformer yields the best validation results, but a randomly initialized transformer with smart design can also be trained to reach accuracies close to that of the pre-trained transformer. Copyright 2021 for this paper by its authors.

Tweet Classification and Sentiment Analysis of Covid 19 Epidemic by Applying Hybrid Based Techniques

World wide spread of COVID-19 pandemic, is throttling the normal life nearly for two years and claiming millions of life all over the globe. Starting from Wuhan of China it crosses more than 200 countries, thereby imposing a overwhelming challenge to health care system. On the other hand, there has been unprecedented advancement of the social media, namely, Twitter, Facebook, WhatsApp and Instagram etc. in an exponential manner. The essence of this paper is to extract and elucidate the opinion or sentiments of the people all around the globe regarding Coronavirus pandemic based on Twitter data. The analysis are based on both lexicon-based approach followed by machine learning algorithms and aims to express the state-of-the-art of the sentiment analysis on the current Coronavirus epidemic prevailing in the entire world and the awareness of the people regarding the disease, its symptoms and impact followed by the preventive measures that need to be undertaken. © 2022 IEEE.

Unsupervised Classification of Covid-19 using Chest X-rays with Convolutional Autoencoder

One of the crucial step in reducing mortality rate due to covid-19 infection is early diagnosis based on examining chest x-rays by trained experts. Particularly, the diagnosis results can only be effective if test results are considerably accurate. Considering the wide spread of covid-19, it can be challenging for certain testing centers to cope with manual examination of x-rays. Moreover, diagnosis errors due to human fatigue can quickly increase and thus put many lives at risk. In this regard, machine learning (ML) has been explored as an alternative to manual evaluation. However, this generally requires the collection of labeled datasets, which can be infeasible due to cost, time or unavailable human resources. As such, to address the aforementioned problem, this paper proposes unsupervised classification of covid-19 from chest x-rays using convolutional autoencoder (CAE), which is further regularized using denoising or dropout training criterion. Our model is light weight, fast and does not require labeled dataset for training the classification model. As such, it is competitively cheaper to deploy in practice. Using a publicly available dataset, several experiments are performed to show the effectiveness of our proposed solution in comparison to other state-of-the-art approaches. Different evaluation metrics such as accuracy, recall, precision and F1-score that are reported show that the proposed model outperforms several state-of-the-art approaches that are more complicated, slower and importantly rely on labeled dataset for training. © 2022 IEEE.

Self-Sovereign Identity and User Control for Privacy-Preserving Contact Tracing

Contact tracing apps use mobile devices to keep track of and promptly identify those who come in contact with an individual who tests positive for COVID-19. However, privacy is a major obstacle to the wide-spread use of such apps since users are concerned about sharing their contact and diagnosis data. This research overcomes multiple challenges facing contact tracing apps: (1) As researchers have pointed out, there is a need to balance contact tracing effectiveness with the amount of user identity and diagnosis information shared. (2) No matter what information the user chooses to share, the app should safeguard the privacy of user information. (3) On the other hand, some essential test result information must be shared for the contact tracing app to work. While contact tracing apps have done a good job maintaining contact information on the user's device, most such apps publish positive COVID-19 test results to a central server which have some risks for compromise. We address these challenges by (1) giving the user the right to choose how much information to share about their diagnosis and their identity, (2) building our novel contact tracing app on top of Self-Sovereign Identity (SSI) to assure privacy preserving user authentication with verifiable credentials, and (3) decentralizing the storage of COVID-19 test results. We, in collaboration with Verizon, have implemented our Privacy-preserving Contact Tracing (PpCT) app, leveraging SSI advances based on the blockchain for their 5G network. © 2021 ACM.

The Role of Artificial Intelligence and Machine Learning in Covid-19 LED Pandemic

The leading life threatening and fatal virus which is spreaded all around the globe causing pandemic Covid-19 tends to originate in the wuhan city of China in Nov 2019 affecting the life of million every single day, Multiple clinical approaches were performed taking in consideration latest technology: AI and Ml have contributed a lot so as to control its wide spread. This paper presents some of the application of AI and ML which will help us to tackle this situation. There various branches of helping hands are the following: helped us by detection and testing of covid-19,building up of smart hospital using ML, mask detection using ML model and maintaining the social distancing and sanitization plays a crucial role for controlling the virus and lastly predicting the anxiety disorder is also important to understand the effects the lockdown has caused.We have also emphasised on the the challenges faced while predicting its accuracy of the model since the dataset wasn't up the mark due to absence of historical data it wasn't proficient, also considering the opportunity this pandemic has brought in our life's by introducing digital platforms facilities in everyday life by improving the quality services. Considering the future scope of this skill oriented technology, the world is going to experience a drastic transformation and we will hope scientists and researchers make utmost use of AI and ML to bring us the best potential resources from it. © 2021 IEEE.

A Deep Learning Based Face Mask and Social Distance Monitoring Framework for Covid-19

Covid-19 has become one of the most dangerous diseases suddenly which is infecting the people in all over the world. It has created an impact on the lives of thousands of people all over the world. Governments of various countries are trying to control the wide spread of COVID-19 in our society. It is a danger to the human existence, so it is highly important to stop spreading the disease as it is deadly contagious. Spreading of virus can be prevented, by maintaining social distance and hygiene. The major transmission mode of COVID-19 is through saliva and nose discharge.It is highly important to know the necessity of wearing mask in the public. We need an automatic monitoring system for time being to monitor the public so as to avoid the situation of spreading of the disease for not wearing mask and not maintaining social distance. We have applied a deep learning technique to check whether the person is having a face mask or not. This work is aimed to identify the face mask in the public places which helps in the reducing of spread of the virus. CNN is used for the model. The proposed model recognizes the face region in the image given as input and extracts the necessary facial features to identify the face mask region. © 2021 IEEE.