Vaccine hesitancy in the post-vaccination COVID-19 era: a machine learning and statistical analysis driven study.

Background The COVID-19 pandemic has badly affected people of all ages globally. Therefore, its vaccine has been developed and made available for public use in unprecedented times. However, because of various levels of hesitancy, it did not have general acceptance. The main objective of this work is to identify the risk associated with the COVID-19 vaccines by developing a prognosis tool that will help in enhancing its acceptability and therefore, reducing the lethality of SARS-CoV-2. Methods: The obtained raw VAERS dataset has three files indicating medical history, vaccination status, and post vaccination symptoms respectively with more than 354 thousand samples. After pre-processing, this raw dataset has been merged into one with 85 different attributes however, the whole analysis has been subdivided into three scenarios ((i) medical history (ii) reaction of vaccination (iii) combination of both). Further, Machine Learning (ML) models which includes Linear Regression (LR), Random Forest (RF), Naive Bayes (NB), Light Gradient Boosting Algorithm (LGBM), and Multilayer feed-forward perceptron (MLP) have been employed to predict the most probable outcome and their performance has been evaluated based on various performance parameters. Also, the chi-square (statistical), LR, RF, and LGBM have been utilized to estimate the most probable attribute in the dataset that resulted in death, hospitalization, and COVID-19. Results: For the above mentioned scenarios, all the models estimates different attributes (such as cardiac arrest, Cancer, Hyperlipidemia, Kidney Disease, Diabetes, Atrial Fibrillation, Dementia, Thyroid, etc.) for death, hospitalization, and COVID-19 even after vaccination. Further, for prediction, LGBM outperforms all the other developed models in most of the scenarios whereas, LR, RF, NB, and MLP perform satisfactorily in patches. Conclusion: The male population in the age group of 50-70 has been found most susceptible to this virus. Also, people with existing serious illnesses have been found most vulnerable. Therefore, they must be vaccinated in close observations. Generally, no serious adverse effect of the vaccine has been observed therefore, people must vaccinate themselves without any hesitation at the earliest. Also, the model developed using LGBM establishes its supremacy over all the other prediction models. Therefore, it can be very helpful for the policymakers in administrating and prioritizing the population for the different vaccination programs.

COVID-19 Detection via Fusion of Modulation Spectrum and Linear Prediction Speech Features

The coronavirus disease 2019 (COVID-19) pandemic has drastically impacted life around the globe. As life returns to pre-pandemic routines, COVID-19 testing has become a key component, assuring that travellers and citizens are free from the disease. Conventional tests can be expensive, time-consuming (results can take up to 48h), and require laboratory testing. Rapid antigen testing, in turn, can generate results within 15-30 minutes and can be done at home, but research shows they achieve very poor sensitivity rates. In this paper, we propose an alternative test based on speech signals recorded at home with a portable device. It has been well-documented that the virus affects many of the speech production systems (e.g., lungs, larynx, and articulators). As such, we propose the use of new modulation spectral features and linear prediction analysis to characterize these changes and design a two-stage COVID-19 prediction system by fusing the proposed features. Experiments with three COVID-19 speech datasets (CSS, DiCOVA2, and Cambridge subset) show that the two-stage feature fusion system outperforms the benchmark systems of CSS and Cambridge datasets while maintaining lower complexity compared to DL-based systems. Furthermore, the two-stage system demonstrates higher generalizability to unseen conditions in a cross-dataset testing evaluation scheme. The generalizability and interpretability of our proposed system demonstrate the potential for accessible, low-cost, at-home COVID-19 testing. IEEE

Observation of Online vs Offline Learning Experience

As the outbreak of COVID-19 increased in various countries. India is also majorly affected with the COVID-19 by that education system is affected, and it has transferred the traditional face-to-face teaching to online education platform. Considering student's perspective on both online and offline learning mode in India, we conducted a survey to collect the data. In that survey questionnaire, focus was on the factors and situation which can affect the education system. Using that data, we used Kruskal Wallis test to collect the evidence for which learning mode is better and Naive Bayes Algorithm, we were able to conclude the results. © 2022 IEEE.

Predictive modelling and analytics of students' grades using machine learning algorithms.

The outbreak of COVID-19 has caused significant disruption in all sectors and industries around the world. To tackle the spread of the novel coronavirus, the learning process and the modes of delivery had to be altered. Most courses are delivered traditionally with face-to-face or a blended approach through online learning platforms. In addition, researchers and educational specialists around the globe always had a keen interest in predicting a student's performance based on the student's information such as previous exam results obtained and experiences. With the upsurge in using online learning platforms, predicting the student's performance by including their interactions such as discussion forums could be integrated to create a predictive model. The aims of the research are to provide a predictive model to forecast students' performance (grade/engagement) and to analyse the effect of online learning platform's features. The model created in this study made use of machine learning techniques to predict the final grade and engagement level of a learner. The quantitative approach for student's data analysis and processing proved that the Random Forest classifier outperformed the others. An accuracy of 85% and 83% were recorded for grade and engagement prediction respectively with attributes related to student profile and interaction on a learning platform.

Climate change and infectious disease: A prologue on multidisciplinary cooperation and predictive analytics.

Climate change impacts global ecosystems at the interface of infectious disease agents and hosts and vectors for animals, humans, and plants. The climate is changing, and the impacts are complex, with multifaceted effects. In addition to connecting climate change and infectious diseases, we aim to draw attention to the challenges of working across multiple disciplines. Doing this requires concentrated efforts in a variety of areas to advance the technological state of the art and at the same time implement ideas and explain to the everyday citizen what is happening. The world's experience with COVID-19 has revealed many gaps in our past approaches to anticipating emerging infectious diseases. Most approaches to predicting outbreaks and identifying emerging microbes of major consequence have been with those causing high morbidity and mortality in humans and animals. These lagging indicators offer limited ability to prevent disease spillover and amplifications in new hosts. Leading indicators and novel approaches are more valuable and now feasible, with multidisciplinary approaches also within our grasp to provide links to disease predictions through holistic monitoring of micro and macro ecological changes. In this commentary, we describe niches for climate change and infectious diseases as well as overarching themes for the important role of collaborative team science, predictive analytics, and biosecurity. With a multidisciplinary cooperative "all call," we can enhance our ability to engage and resolve current and emerging problems.

Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA.

Objective: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. Materials and Methods: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. Results: Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. Discussion: Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. Conclusion: This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana.

Estimation of Short-Time Forecast for Covid-19 Outbreak in India: State-Wise Prediction and Analysis

Covid-19 pandemic is of major concern that largely impacts the human and growth of respective countries. Countries like India also tried their best to manage this Covid outbreak situation through lockdown and handle its growth through strict relaxation using zonal distribution strategy. An urge of proper estimation for this outbreak is required, which can be beneficial in arrangement of proper healthcare facilities in different states of the country. India has wide diversity between its states. The effect of temperature and dense population have been two key parameters that have been poorly studied with respect to each state. In this paper, we tried to forecast the number of Covid-19 cases (8 Jan 2020 to 25 April 2020) using Kalman filter at state and national levels to generate various trends and patterns. Our analysis has been evaluated on four classification of states: most affected, moderate affected, least affected and pandemic free states. The results have been collected on vulnerable temperature parameters (historical and forecast data) of each state. The national level estimates are further compared with other countries like United States of America, Spain, France, Italy and Germany through confirmed, recovered and death cases. In the current lockdown situation our estimation shows that India should expect as many as 60,140 cases by May 24, 2020. The trends achieved shows that India has been found to be one of the beneficiaries of lockdown decisions but failed at some places in its regions due to social activities, huge dense population and temperature variation. This study will be beneficial for different state level bodies to manage various health care resources between its states or can support intra-state and can start their administrative functionality accordingly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predictive Analysis of COVID-19 Data Using Two-Step Quantile Regression Method

In the year 2019, research community began with new challenge called novel coronavirus disease (COVID-19) and has opened up new challenges for the research community. From the report of the World Health Organization (WHO), the new virus COVID-2019 (World Health Organization (2020) Coronavirus disease 2019 (COVID-19): situation report, p 67) causes dangerous illness to the concerned person, and it spread to other peoples with huge rate through contact. Such kind of pandemic analysis needs efficient methods to predict data and also helped further to analyze such epidemic risks. These kinds of analyses are used to handle and control the epidemic kind of diseases. Regression analysis is kind of ML methods and is worked well to analyze such kind of epidemic data. The work in this paper about analysis of COVID-19 data especially focused in the state of Andhra Pradesh. First, the data are collected from the website (i.e., https://prsindia.org/ ). Next, we applied various regression techniques like linear, multi-linear and quantile regression for COVID-19 data for the prediction of cases. Further extended work to derive penalized quantile results using lasso. The results shows that the two-step quantile regression (TSQR) has been shown to be a better predictive method for predicting confirmed cases compared to linear and multi-linear regressions in terms of MSE and R-Score parameters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Emerging health start-ups for economic feasibility: opportunities during COVID-19

The Indian population has a potential threat of communicable and noncommunicable diseases. The low preventive health measure is a cause of significant loss to the economy. The integration of the cloud platform with remote wearable sensors not only helps the health stakeholders to capture the patient’s vitals but also perform predictive analysis during COVID-19. Raising timely alarms through the Internet of Medical Things and artificial intelligence (AI) has complete preventive care applications through real-time analytics. However, health merchandise start-ups using AI and machine learning for timely device delivery face delay in making themselves available and affordable for remote patients of Tiers II and III. This study takes a health service provider perspective and seeks to study the problem situation using a causal loop model. Finally, analysis of the feedback loops and medical device import data is done to develop suitable strategies for COVID-19 patients of remote locations. © 2022 Elsevier Inc. All rights reserved.

Impact analysis of COVID-19 news headlines on global economy

News articles have a strong effect on the readers’ sentiments, which in turn affects stock markets and the way economies of various countries perform. This chapter’s main idea is based on the efficient market hypothesis that highlights the conjunction of news and information with market performance. The work presented in this chapter analyzes the impact of news headlines on markets and how they affect the global economy during the unprecedented COVID-19 pandemic. This chapter proposes to use the lexicon method to calculate the sentiment values of the news headlines. Based on these values, stock index values are predicted using machine learning algorithms. The chapter predicts the effect of Indian news headlines on the Nifty index. This can then be extrapolated to global economies as well. © 2022 Elsevier Inc. All rights reserved.

Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Excess diabetes mellitus-related deaths during the COVID-19 pandemic in the United States.

Background: Diabetes mellitus (DM) is a critical risk factor for severe SARS-CoV-2 infection, and SARS-CoV-2 infection contributes to worsening glycemic control. The COVID-19 pandemic profoundly disrupted the delivery of care for patients with diabetes. We aimed to determine the trend of DM-related deaths during the pandemic. Methods: In this serial population-based study between January 1, 2006 and December 31, 2021, mortality data of decedents aged ≥25 years from the National Vital Statistics System dataset was analyzed. Decedents with DM as the underlying or contributing cause of death on the death certificate were defined as DM-related deaths. Excess deaths were estimated by comparing observed versus expected age-standardized mortality rates derived from mortality during 2006-2019 with linear and polynomial regression models. The trends of mortality were quantified with joinpoint regression analysis. Subgroup analyses were performed by age, sex, race/ethnicity, and state. Findings: Among 4·25 million DM-related deaths during 2006-2021, there was a significant surge of more than 30% in mortality during the pandemic, from 106·8 (per 100,000 persons) in 2019 to 144·1 in 2020 and 148·3 in 2021. Adults aged 25-44 years had the most pronounced rise in mortality. Widened racial/ethnic disparity was observed, with Hispanics demonstrating the highest excess deaths (67·5%; 95% CI 60·9-74·7%), almost three times that of non-Hispanic whites (23·9%; 95% CI 21·2-26·7%). Interpretation: The United States saw an increase in DM-related mortality during the pandemic. The disproportionate rise in young adults and the widened racial/ethnic disparity warrant urgent preventative interventions from diverse stakeholders. Funding: National Natural Science Foundation of China.

Emerging real time analytics based health start-ups: opportunities during covid-19

Indian population has potential threat of communicable and non-communicable diseases. The low preventive health measure is a cause of major loss to the economy. Integration of the cloud platform with remote wearable sensors not only helps the health stakeholders to capture the patient vitals but also perform predictive analysis during COVID-19. Raising timely alarms through Internet of Medical Things and Artificial Intelligence has wide application in preventive care through real time analytics. However, Health Merchandise Startups using artificial intelligence and machine learning for timely device delivery face delay in making themselves available and affordable for Remote patients of Tier II and III. This study takes a health service provider perspective and seeks to study problem situation systemically by using a casual loop model. Finally, analysis of the feedback loops is done to be able to come out with suitable strategies for COVID-19 patients of Remote locations. © MEDIC SCIENTIFIC, All rights reserved.

An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic.

The world still suffers from the COVID-19 pandemic, which was identified in late 2019. The number of COVID-19 confirmed cases are increasing every day, and many governments are taking various measures and policies, such as city lockdown. It seriously treats people's lives and health conditions, and it is highly required to immediately take appropriate actions to minimise the virus spread and manage the COVID-19 outbreak. This paper aims to study the impact of the lockdown schedule on pandemic prevention and control in Ningbo, China. For this, machine learning techniques such as the K-nearest neighbours and Random Forest are used to predict the number of COVID-19 confirmed cases according to five scenarios, including no lockdown and 2 weeks, 1, 3, and 6 months postponed lockdown. According to the results, the random forest machine learning technique outperforms the K-nearest neighbours model in terms of mean squared error and R-square. The results support that taking an early lockdown measure minimises the number of COVID-19 confirmed cases in a city and addresses that late actions lead to a sharp COVID-19 outbreak.

Predictive Analysis of the Pro-Environmental Behaviour of College Students Using a Decision-Tree Model.

The emergence of the COVID-19 pandemic has hindered the achievement of the global Sustainable Development Goals (SDGs). Pro-environmental behaviour contributes to the achievement of the SDGs, and UNESCO considers college students as major contributors. There is a scarcity of research on college student pro-environmental behaviour and even less on the use of decision trees to predict pro-environmental behaviour. Therefore, this study aims to investigate the validity of applying a modified C5.0 decision-tree model to predict college student pro-environmental behaviour and to determine which variables can be used as predictors of such behaviour. To address these questions, 334 university students in Guangdong Province, China, completed a questionnaire that consisted of seven parts: the Perceived Behavioural Control Scale, the Social Identity Scale, the Innovative Behaviour Scale, the Sense of Place Scale, the Subjective Norms Scale, the Environmental Activism Scale, and the willingness to behave in an environmentally responsible manner scale. A modified C5.0 decision-tree model was also used to make predictions. The results showed that the main predictor variables for pro-environmental behaviour were willingness to behave in an environmentally responsible manner, innovative behaviour, and perceived behavioural control. The importance of willingness to behave in an environmentally responsible manner was 0.1562, the importance of innovative behaviour was 0.1404, and the perceived behavioural control was 0.1322. Secondly, there are 63.88% of those with high pro-environmental behaviour. Therefore, we conclude that the decision tree model is valid in predicting the pro-environmental behaviour of college student. The predictor variables for pro-environmental behaviour were, in order of importance: Willingness to behave in an environmentally responsible manner, Environmental Activism, Subjective Norms, Sense of Place, Innovative Behaviour, Social Identity, and Perceived Behavioural Control. This study establishes a link between machine learning and pro-environmental behaviour and broadens understanding of pro-environmental behaviour. It provides a research support with improving people's sustainable development philosophy and behaviour.

Psychological Distress, Anxiety, and Academic Self-Efficacy as Predictors of Study Satisfaction Among Peruvian University Students During the COVID-19 Pandemic.

The objective of this research study was to determine if psychological distress, anxiety, and academic self-efficacy predict satisfaction with studies in Peruvian university students during the COVID-19 pandemic. A cross-sectional and predictive design was used, in which 582 Peruvian university students participated, 243 men and 339 women, between the ages of 16 and 41. Student's t-statistics were used to analyze the differences in scores of psychological distress, anxiety, academic self-efficacy, and satisfaction with studies based on the sex of the participants, Pearson's R was used for the analysis of correlations between variables, and multiple linear regressions were used to evaluate the predictive model. In the analyses, the significance level was set at 0.05. The results show that men have higher levels of psychological distress, anxiety, and academic self-efficacy than women do (p < 0.01); high levels of psychological distress correlate with high levels of anxiety (r = 0.580, p < 0.01) and low levels of satisfaction with studies (r = -0.178, p < 0.01) and academic self-efficacy (r = -0.348, p < 0.01); high levels of anxiety correlate with low levels of satisfaction with studies (r = -0.122, p < 0.01) and academic self-efficacy (r = -0.192, p < 0.01); and high levels of academic self-efficacy correlate with high levels of satisfaction with studies (r = 0.429, p < 0.01). Academic self-efficacy was also found to predict satisfaction with studies (ß = 0.429, p < 0.01). This concludes that, although there are significant correlations between psychological distress, anxiety, academic self-efficacy, and satisfaction with studies, academic self-efficacy is the variable that most predicts satisfaction with studies in Peruvian university students.

Additive Penalized based Quantile Regression (APQR) for Predictive Analytics for Pandemic Data

In present, entire globe facing global challenges due to the pandemic situations raised by the known COVID-2019. Also, this can be impacted to most issues which can includes financial crises, medical emergency, education loss, chronic hunder, migration of people, etc. It can not only threat to the pre-existing people suffered with health issues also to the healthy people who are more conscious towards health. However, there is a necessity to apply a very good statistical analysis over such kind of issues. Moreover, many of the research works towards this problem entire globe. One of the popular techniques to apply statistical analysis to such kind of pandemic data is quantile regression. Need an extension version to the quantile regression to provide solution to the pandemic data analysis. Moreover, it is required to know prior the overall idea of the conditional distribution of a response variable. A penalized based quantile regression utilizes the minimization of the L1 norm to address heterogeneous pandemic data prediction. The study focused on minimization of L1 norm to effectively address such pandemic with additive penalized model for quantile regression known as APQR. The proposed model can help to find regression coefficients and control heterogeneity effectively. A numerical study using simulated and real examples demonstrates the competitive performance of the proposed APQR would be helpful and recommended for pandemic data prediction than standard quantile methods. © 2022, Mathematical and Research Society. All rights reserved.

Efficient Automated Disease Diagnosis Using Machine Learning Models

A.I. (ML) is a part of computerized reasoning and software engineering that spotlights the utilization of information and calculations to duplicate the way that people learn, bit by bit working on its precision. A.I. is a significant part of the creating field of data science. Utilizing simple strategies, computations are ready to make plans or figures, revealing critical encounters inside data mining projects. These pieces of information drive dynamic inside applications and organizations, ideally influencing critical advancement estimations. As enormous information continues to expand and create the market revenue for data analysts will augment, anticipating that they should help the I.D. of the most applied-link business questions and subsequently the data to react to them. By utilizing a machine learning (ML) calculation, the proposed work will recognize COVID-19 and disease patients in danger of creating cardiovascular breakdown. Proof has shown that COVID-19 and Cancer can contrarily affect the heart framework, leaving patients in peril for chances like cardiovascular assault, upheld surprising heartbeats, coronary disappointments, and death [2]. As a result of the expanded danger for these inconveniences, there is a critical need to distinguish COVID-19 patients at danger for heart problems, yet these prescient capacities don’t presently exist. The proposed work will give early signs and guarantee that assets are given to the patients at early need. Right away, we wanted to gather information from COVID-19 and disease patients. Likewise, we wanted cardiovascular explicit research facility tests, constantly acquired crucial signs, and imaging information like C.T. filters, Pet Scan, ECG (echocardiography). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparative Predictive Analysis of Mortality Rate after Covid-19 Vaccination Using Various Machine Learning Approaches

This work has mainly targeted in performing comparative real time predictive analysis of mortality rate after having COVID-19 vaccination using different machine learning approaches. In this paper various deep learning models viz. RNN, LSTM and CNN have been utilized to make future prediction on mortality rate on the basis of administered vaccine doses. Firstly, the dataset of confirmed active cases, death cases and administered vaccine doses have been converted from time-series format to supervised learning format, and secondly different deep learning models have been trained and compared based on the transformed dataset. The prediction analysis is performed strictly based on the newest COVID-19 Delta Variant infected cases. The predictive analysis has resulted 15.53% of reduction in mortality rate and 24.67% of reduction in confirmed active cases with increase in vaccination rate. © 2022 IEEE.

Exploration of time series model for predictive evaluation of long-term performance of membrane distillation desalination

Owing to the inherent complications in membrane distillation (MD) operations, it has become a challenge to acknowledge swiftly and appropriately to safeguard the quality of effluent, particularly when the processing cost is a prominent concern. Membrane wetting in MD operations is a major concern during longterm performance. In this study, machine learning (ML) methodologies were utilized to overcome the limitations of conventional mechanistic modeling. ML applications have never been explored to investigate how operational factors, such as water flux and salt flux, are affected during long-term MD performance. Furthermore, time-dependent factors were neglected, making it difficult to analyze the relationship between effluent quality and operational factors. Therefore, this study demonstrates a novel ML-based framework designed to enhance the performance of MD. The ML-based framework consists of an autoregressive integrated moving average (ARIMA) and utilizes a unique pathway to explain the impact of time series among operational factors. The accuracy of forecasting has been explored by utilizing 180 h (180 datasets), that was further used and divided into training (165 datasets) and test datasets (15 datasets). Eventually, the ARIMA model demonstrated a highly precise relationship order between the model and experimental data, which can be further used to forecast membrane performance in terms of wetting and fouling. The selected ARIMA model (3,2,1) appears to be an adequate model for water and salt flux data which has been effectively used to capture the course of permeate water and salt flux by producing the smallest forecast RMSE. The RMSE values were observed to be 0.22 and 0.05 for water and salt flux respectively, which can better predict long time series with high frequency. These frameworks can be applied for the early prediction of membrane wetting if ample high-resolution data are available.(c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.