Omicron Case Prediction using Machine Learning

It's been over two years that the world has been dealing with the novel Coronavirus Disease 2019 (COVID-19). It has rocked the world in the face of another major outbreak. Countries have undergone various lockdowns curfews in their own ways, which certainly has impacted our daily lives. COVID-19 has undergone various mutations till now. It is responsible for the spikes in COVID-19 cases across the world. The latest variant 'Omicron'., labeled as B.1.1.529, has been marked as a Variant of Concern by the World Health Organization (WHO). It has been proven to be the most infectious, but less deadly as of now. This paper attempts to propose an analysis and prediction of Omicron daily cases in India using SARIMA Exponential Smoothing Machine Learning models. Both of these machine learning models are based on the time series forecasting concept and rely on previous data to predict future outcomes. © 2022 IEEE.

TabNet Based Prediction Model for ICU admission in Covid-19 patients

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the responsible virus for coronavirus disease 2019 (COVID-19). It was reported the first time in Wuhan (China) by late December 2019. The COVID-19 pandemic has become a global health risk due to the urgent need for an Intensive Care Unit (ICU) that exceeded its capacity. To cope with this exponential spread the fast adoption of Artificial Intelligence (AI) tools and advanced technology is crucial. For this reason, many research works in AI are conducted. In the current paper, we intend to report AI applications and solutions based on machine learning, deep learning, and data mining algorithms for detecting, predicting, and diagnosing COVID-19. Furthermore, this study aims to develop a new deep learning-based method capable of predicting whether a COVID-19 patient requires admission to an intensive care unit using clinical tabular data from Kaggle. This model will contribute to the optimization of ICU resources. The experimental results showed that combining Synthetic Minority Oversampling Technique (SMOTE) and TabNet classifier improved the prediction performance and surpassed the state-of-the-art models: MLP, RF, LR, and KNN. © 2022 IEEE.

A Dynamic Theory of Security Free-Riding by Firms in the WFH Age

The COVID-19 pandemic has radically transformed the work-from-home (WFH) paradigm, and expanded an organization's cyber-vulnerability space. We propose a novel strategic method to quantify the degree of sub-optimal cybersecurity in an organization of employees, all of whom work in heterogeneous WFH 'siloes'. Specifically, we model the per-unit cost of asymmetric WFH employees to invest in security-improving effort units as time-discounted exponential martingales over time, and derive as benchmark - the centrally-planned socially optimal aggregate employee effort at any given time instant. We then derive the time-varying strategic Nash equilibrium amount of aggregate employee effort in cybersecurity in a distributed setting. The time-varying ratio of these centralized and distributed estimates quantifies the free riding dynamics, i.e., security sub-optimality, within an organization. Rigorous estimates of the degree of sub-optimal cybersecurity will drive organizational policy makers to design appropriate (customized) solutions that voluntarily incentivize WFH employees to invest in required cybersecurity best practices. © 2022 IEEE.

Identifying the Dimensions of Teachers' Digital Learning Agility in the Age of Exponential Technology Use

The volatile, uncertain, complex, and ambiguous (VUCA) world and IR4.0 developments forces drastic changes to sustain and provide quality education. When schools were shut down abruptly due to COVID-19, teachers were forced into emergency remote teaching, mostly by utilizing technologies but with little to no specific structure. In Malaysia, studies found that teachers struggled with technology ability especially in mastering technology applications. Due to limited experience in preparing electronic materials and using online platforms, teachers took the time to deliberate on the ways to teach online, causing delays in learning. Delays can be mitigated if teachers are agile. Agile teachers are capable to deal with new experience flexibly and rapidly by trying new behaviors and making quick adjustments so that new learning can be realized even when they do not know exactly what to do when they face unexpected challenges. This quality in teachers is important to curb learning loss especially when education was threatened by COVID-19. Reciprocally, technology plays an important role to promote Learning Agility among teachers, ensure sustainability and quality of learning, and forge learners' engagement. With the exponential use of technologies, teachers need to be an agile classroom leader. This study aims at identifying the dimensions that shape teachers' Digital Learning Agility. We hoped that this proposed research can shed insights on digital learning agility and can improve teachers' performance especially in the age of exponential technology use. © ICCE 2022.All rights reserved.

NON-LINEAR OPERATOR APPROXIMATIONS FOR INITIAL VALUE PROBLEMS

Time-evolution of partial differential equations is fundamental for modeling several complex dynamical processes and events forecasting, but the operators associated with such problems are non-linear. We propose a Padé approximation based exponential neural operator scheme for efficiently learning the map between a given initial condition and the activities at a later time. The multiwavelets bases are used for space discretization. By explicitly embedding the exponential operators in the model, we reduce the training parameters and make it more data-efficient which is essential in dealing with scarce and noisy real-world datasets. The Padé exponential operator uses a recurrent structure with shared parameters to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession. We show theoretically that the gradients associated with the recurrent Padé network are bounded across the recurrent horizon. We perform experiments on non-linear systems such as Korteweg-de Vries (KdV) and Kuramoto-Sivashinsky (KS) equations to show that the proposed approach achieves the best performance and at the same time is data-efficient. We also show that urgent real-world problems like epidemic forecasting (for example, COVID-19) can be formulated as a 2D time-varying operator problem. The proposed Padé exponential operators yield better prediction results (53% (52%) better MAE than best neural operator (non-neural operator deep learning model)) compared to state-of-the-art forecasting models. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

On T-Spherical Fuzzy Hypersoft Sets and Their Aggregation Operators with Application in Soft Computing

In the decision sciences problems, systematic evaluation of information containing incompleteness and impreciseness having the feature of parametrization is one of the substantial features. In the present communication, a new notion of T-spherical fuzzy hypersoft set (TSFHSS) has been introduced which contains an additional capacity of accommodating the components of neutral membership (abstain) and refusal compared to intuitionistic fuzzy hypersoft set under the sub-parametrization in an exponential way. Some of the basic operations on T-spherical fuzzy hypersoft set and some important aggregation operators have been presented and studied in detail. Further, in order to exhibit an application in the field of soft computing, the selection problem of COVID-19 mask has been numerically illustrated with some advantageous and concluding remarks. © 2022 IEEE.

Related Return Patterns Adjustment Under the Impact of COVID-19 on Hedge Funds

The latest threat to global health is an ongoing outbreak of a respiratory disease known as COVID-19 and has become a global concern. The exponential spread of the COVID-19 pandemic shook up global markets and caused major adjustments to the world economy. In this paper, we investigate whether these changes affected hedge fund return patterns. We decompose hedge fund index returns into Fama-French factors using data from 2017 - 2019 and compare it to decompositions using data from 2020 and 2021 to date. Our empirical results suggest that the Fama-French factor exposures changed on the conventional hedge funds. This has reflected that COVID-19 has an impact on the return patterns of the hedge funds we selected. The findings have implications for investors and major players in the investment markets. Our research is useful for predicting how the performance of hedge funds changes in market disruption. © 2022 ACM.

Impact of Vaccination Policies for COVID-19 Using Hybrid Simulation

A stochastic model for individual immune response is developed. This model is then incorporated in a larger simulation model for the spread of COVID-19 in a population. The simulator allows random transitions between being susceptible, exposed, having mild or severe symptoms, as well as random non-exponential sojourn times in those states. The model is more efficient than others based on geographical location, where the virus spreads according to actual distance between individuals. We are able to simulate much larger populations and vary parameters such as time between vaccinations, probability of infection, and so on. We present an application to study the effects on healthcare as a function of vaccination policies. © 2022 IEEE.

"SOS Signal" in Breathing Sound - Rapid COVID-19 Diagnosis Based on Machine Learning

The severe acute respiratory syndrome coronavirus 2 is a novel type of coronavirus that causes COVID-19. The COVID-19 virus has recently infected more than 590 million individuals, resulting in a global pandemic. Traditional diagnosis methods are no longer effective due to the exponential rise in infection rates. Quick and accurate COVID-19 diagnosis is made possible by machine learning (ML), which also assuages the burden on healthcare systems. After the effective utilization of Cough Audio Signal Classification in diagnosing a number of respiratory illnesses, there has been significant interest in using ML to enable universal COVID-19 screening. The purpose of the current study is to determine people's COVID-19 status through machine learning algorithms. We have developed a Random Forest based model and achieved an accuracy of 0.873 on the COUGHVID dataset, demonstrates the potential of using audio signals as a cheap, accessible, and accurate COVID-19 screening tool. © 2022 ACM.

Early-confinement strategy to tackling COVID-19 in Morocco, a mathematical modelling study

Morocco is among the countries that started setting up confinement in the early stage of the COVID-19 spread. Comparing the number of cumulative cases in various countries, a partial lock-down has delayed the exponential outbreak of COVID-19 in Morocco. Using a compartmental model, we attempt to estimate the mean proportion of correctly confined sub-population in Morocco as well as its effect on the continuing spread of COVID-19. A fitting to Moroccan data is established. Furthermore, we have highlighted some COVID-19 epidemic scenarios that could have happened in Morocco after the deconfinement onset while considering a different combination of preventive measures. © The authors. Published by EDP Sciences, ROADEF, SMAI 2022.

Heart Rate Variability of Healthcare Workers during Covid-19

The outbreak of Covid-19 has exacerbated the mental health of Healthcare Workers (HCWs), caused by an increase in their stress levels owing to an exponential rise in their workloads. Previous works have revealed visible changes in Heart Rate Variability (HRV), in response to increased/decreased stress levels. This study focused on analyzing HRV as a parameter to observe the impact of higher stress levels, on clinicians, due to the pandemic. Their responses to a Perceived Stress Score (PSS) questionnaire were used as a reference to determine their escalated stress levels. The responses showed that 40% of clinicians revealed increased levels of high chronic stress while the remaining were affected by moderate chronic stress. We computed HRV for each clinician from HR data obtained using a chest-based wearable device during sleep and ward sessions. Through detailed analysis of HRV, we observed clinicians with high chronic stress showed lower HRV when compared to clinicians with moderate chronic stress during both sleep and ward sessions. Later we did a close investigation of their HRV on Day 1 and Day 2 in Covid-IP (Inpatient) and compared the HRV features. Finally, we compared the HRV features of clinicians between Covid-IP Covid-OP (Outpatient) ward sessions. The above study validated that HRV is a reliable parameter for an objective assessment of stress levels. © 2022 IEEE.

Understanding and Analysing the Spread Pattern of Covid-19

The chapter here defines the exponential trajectory of the growth dynamics of the disease. From infection growth trend to recovery and casualty growth rate, all follow an exponential rise with respect to time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Decision Making Approach for Street Lockdown to Cope with Covid-19 Cases by Using Shortest Path Selection Mechanism for Unplanned Colonies

Infectious disease syndrome like covid-19 falls under the Public health domain and needs to be addressed with timely decisions and rapid actions. For such diseases, the dispersal becomes exponential with frequent social gatherings, therefore the immediate strategy, to control the surging waves of covid-19, was to impose immediate lockdown of COVID-19 infected zones. In this paper, the concept of street networks has been incorporated with shortest path algorithm e.g. minimum spanning tree (MST) to define an approach to investigate the correlation between reported COVID-19 cases and relevant streets in order to adopt better lockdown strategy for unplanned colonies. Geo-spatial representation has been used for subsequent composition of patterns to identify the particular streets for locked down. Results show that MST provides better solution by evaluating explicit areas of concern for lockdown plans. © 2022 IEEE.

Network Analysis of German COVID-19 Related Discussions on Telegram

We present an effective way to create a dataset from relevant channels and groups of the messenger service Telegram, to detect clusters in this network, and to find influential actors. Our focus lies on the network of German COVID-19 sceptics that formed on Telegram along with growing restrictions meant to prevent the spreading of COVID-19. We create the dataset by using a scraper based on exponential discriminative snowball sampling, combining two different approaches. We show the best way to define a starting point for the sampling and to detect relevant neighbouring channels for the given data. Community clusters in the network are detected by using the Louvain method. Furthermore, we show influential channels and actors by defining a PageRank based ranking scheme. A heatmap illustrates the correlation between the number of channel members and the ranking. We also examine the growth of the network in relation to the governmental COVID-19 measures. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Reliability Investigation of Contact Tracing using Resource-Constrained Mobile Devices

During this pandemic time while finding the (SARS-CoV-2) infected person many of the applications got active where various nations participated actively. The main device involved in the whole process is Smartphone. The existing applications are focusing on the use of Bluetooth technology. Bluetooth is limited with the area it can cover and noise it produces to broadcast the messages to the neighbors. Also, while searching the position of the infected person one concern could be that is Position of the smartphone accurate? or for how long the tracing will happen? While tracing the position of the person whether infected or not infected, the compromise cannot be done. At the pandemic situation, the little mistake of the position will cost the life of a person and the growing number of infected persons will yield to an exponential cost. Also, the life of the smartphone to keep working needs energy through battery. Continuous localization will need continuous flow of the energy for that device. Thus, the smartphone needs to be charged after a period of time. So, when a person is in a public place, he will need his smartphone to be active. Our main concern with the whole paper is to find the solution through the simulation for the position accuracy of the smartphone as well as to manage the energy consumption. © 2022 IEEE.

Lockdown Phases and Changing Clusters of Indian States with Respect to Number of Cases of COVID-19

The novel coronavirus (COVID-19) incidence in India is currently experiencing exponential rise with apparent spatial variation in growth rate and doubling time. We classify the states into five clusters with low- to high-risk category and identify how the different states moved from one cluster to the other since the onset of the first case on January 30th, 2020, till the end of November 30th, 2020. Result clearly shows the impact of the lockdown and the unlock phases in the changing formation of the clusters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

E-learning teacher digital competence: Construct validation of a scale in times of pandemic

The exponential advance of technology is a determining aspect for teaching practice;Above all, now in the new conditions generated by the Covid-19 pandemic. Suddenly, teachers were forced to incorporate digital e-learning skills to ensure pedagogical processes that are effective in today's virtual conditions. That is why the objective of the study is to carry out the construct validation of a scale to measure the digital competence of the e-learning teacher that is relevant for the pedagogical performance in the new conditions generated by the Covid-19 pandemic. This is an empirical study that has involved a series of statistical procedures applied from a sample of 91 teachers belonging to ten educational institutions of Regular Basic Education in Lima, Peru. Among the main results there is a reliability of the Cronbach's Alpha of 0.956. Likewise, through confirmatory factor analysis, construct validity was guaranteed, demonstrating that the instrument would be suitable to be applied in future diagnoses. © 2021 IEEE.

IoT Based Patient Safety Monitoring System in Hospitals

The exponential surge in India's coronavirus infections over the past months has swamped the health care system, which limited the supply of medical oxygen cylinders. Dozens of hospitals in several Indian cities and towns have run short of oxygen cylinders and also lack continuous monitoring of patients due to labor shortage and patient admitted exponentially. This leads to the lack of attention to patients who advanced to critical complications. To overcome this, it is proposed to automatically measure the pulse rate, the oxygen level in the cylinder, and glucose level by weight with the help of a microcontroller and load cell. The real-time data send to hospital management to change or resupply. It will lead to the continuous monitoring of the patient and reduce the risk. The proposed method will ensure patient safety and also has alert the doctors if any unforeseen problems or accident occurs. © 2022 IEEE.

Exponentially Biased Discriminant Analysis Based Classification of Covid 19 Chest Images Using Generalized Regression Neural Network

Classification is always an interesting problem in the field of computer vision. In a two class problem, there will be an uncertainty in the classification of adjacent images of two classes. To avoid this uncertainty, an exponentially biased discriminant analysis is proposed for the classification. Initially, the entire database is projected to an exponentially biased space. In this space the data is more separated than the original space. Discriminant analysis is then used to classify the objects in this new space. After the training, the test data are approximated to this space using Generalized Regression Neural Network. The proposed algorithm is evaluated using the database of Covid 19 chest images. A better accuracy is observed for the proposed method by comparing with the normal discriminant analysis. But, this accuracy may not be a very good value. Better scientific approaches on the selection of the exponential biasing may give better classification accuracy. © 2021 IEEE.

Machine Learning Based Prediction of COVID-19 Infection in India

The novel coronavirus (COVID-19) infection had spread throughout the globe since the beginning of 2020 giving rise to a pandemic situation. In this paper, attempts have been made to model the COVID-19 infection in India using exponential, logistic and Gompertz-based mathematical machine learning regression models. These predictive methods show an excellent fit with the daily count of confirmed cases for the period between January 30, 2020, and February 3, 2021. The mean squared logarithmic error (MSLE) of the Gompertz model being lowest among the three machine learning regression methods considered in this paper making it ideal at least as a case study for future predictions in Indian scenario. Nevertheless, the epidemiologists, healthcare personnel, or other Government authorities may use this study as a reference for future planning in prevention of such pandemic situation in similar developing nations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.