ABSTRACT
There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society's lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model;it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
The term work-life balance can be described as a path to manage stresses and burnouts in the workplace. In this Covid-19 pandemic, work-from-home practice includes both personal and professional spaces as employees, more often, stay digitally connected. As a result, personal life hardly can be separated, which will potentially create imbalanced life, which creates problems regarding physical and mental health of the employees. In such unprecedented situations, we are required to maintain and/or integrate balanced work-life. A balanced work-life gives employees a stress-free environment to work and improves employees' mental and physical health conditions and relationships. In this study, we focus on maintaining a proper work-life balance through a monitoring tool, the 'Wheel of Life.' Considering the drastic changes in work culture (due to Covid-19, for example), we introduce an interactive interface based on 'Wheel of life' concept. Our interface helps tune various important factors, such as business, creative, social, love and life purpose, and provides multiple recommendations. The purpose of the study is to assist web users to balance their work-life, improve psychological well-being and quality of life in this unforeseen situation.
ABSTRACT
Background: Allogeneic hematopoietic stem cell transplantation activity is growing globally as one of the curative treatment options for many hematological diseases. A stem cell transplant registry plays an important role in such treatment. Setting up a functional stem cell donor registry is quite challenging with several issues such as resources, donor recruitment, donor attrition, ethnicity, lack of support, and impact of coronavirus disease 2019 (COVID-19). Aim(s): The aim of the current study was to present the experience of a resource-constrained registry in India as well as the effect of COVID-19 on its operations. Settings and Design: The present study was a descriptive study which was designed to study the functioning of a resource-constrained registry from north India. Material(s) and Method(s): The study data for the period of 2012-2020 pertaining to donor recruitment, number of searches, number of matched donors, number of transplants performed, and donor attrition was collected from the registry software "Prometheus." Statistical Analysis: Descriptive statistics such as frequency and percentage was used. Result(s): During the past 9 years of operation, the registry has faced several issues pertaining to lack of funds, donor recruitment, donor attrition, and COVID-19 has exacerbated their pain points significantly. The registry has recruited a total of 20,093 donors, of which only 7794 have been human leukocyte antigen typed, with the remaining samples awaiting funding. Out of this small number of typed donors, registry has performed 15 matched unrelated donor transplants for Indian and international patients. As a result of COVID-19, donor attrition was on the rise and showed a peak in 2020. During the year 2020, the number of searches, donor recruitment camps, and donors all decreased considerably. Conclusion(s): The establishment and operation of a stem cell transplant registry necessitate extensive planning and resources. The resource-constrained registries face a number of issues pertaining to effective functioning and future developments. The external support and awareness for the cause can help minimize the pain points of these registries. Copyright © 2022 Indian Journal of Transplantation.
ABSTRACT
The term work-life balance can be described as a path to manage stresses and burnouts in the workplace. In this Covid-19 pandemic, work-from-home practice includes both personal and professional spaces as employees, more often, stay digitally connected. As a result, personal life hardly can be separated, which will potentially create imbalanced life, which creates problems regarding physical and mental health of the employees. In such unprecedented situations, we are required to maintain and/or integrate balanced work-life. A balanced work-life gives employees a stress-free environment to work and improves employees' mental and physical health conditions and relationships. In this study, we focus on maintaining a proper work-life balance through a monitoring tool, the ‘Wheel of Life.' Considering the drastic changes in work culture (due to Covid-19, for example), we introduce an interactive interface based on ‘Wheel of life' concept. Our interface helps tune various important factors, such as business, creative, social, love and life purpose, and provides multiple recommendations. The purpose of the study is to assist web users to balance their work-life, improve psychological well-being and quality of life in this unforeseen situation. © 2022, Universidad Internacional de la Rioja. All rights reserved.
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Researches and Applications of Artificial Intelligence to Mitigate Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and Technology offers readers an interdisciplinary view of state-of-art research related to the COVID-19 outbreak, with a focus on tactics employed to model the number of cases of COVID-19 (time series modeling), models employed to diagnostics COVID-19 based on images, and the panoramic of COVID-19 since its discovery and up to this book's publication. This book showcases the algorithms and models available to manage pandemic data, the role of AI, IoT and Mathematical Modeling, how to prevent and fight COVID-19, and the existing medical, social and pharmaceutical support. Chapters cover methods and protocols, the basics and history of diseases, the fast diagnosis of disease with different automated algorithms and artificial intelligence tools and techniques, the methods of handling epidemiology for mitigating the spread of disease, artificial intelligence and mathematical modeling techniques, and how mental and physical health is affected with social media usage. © 2021 Elsevier Inc. All rights reserved.
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Purpose: The Cook & Move for Your Life randomized pilot study assessed the feasibility and relative efficacy of two dose levels of a remotely-delivered diet and physical activity (PA) intervention for breast cancer (BC) survivors. Methods: Women with a history of stage 0-III BC who were >60 days post-treatment, ate <5 servings per day of fruits/vegetables or engaged in <150 minutes per week of moderate to vigorous physical activity (MVPA), and had smartphone or computer access were enrolled. Participants were randomized to receive one of two doses of an online diet and PA didactic and experiential program, with outcomes measured at 6 months. The low-dose arm received a single 2-hour Zoom session delivered by a dietitian, a chef, a culinary educator, and an exercise physiologist;the high-dose arm received 12 2-hour Zoom sessions over 6 months. All participants received weekly motivational text messages, a Fitbit to self-monitor PA, and study website access. The primary objective was to evaluate overall feasibility based on accrual, adherence, and retention. Prespecified feasibility endpoints were 75% retention at 6 months and 60% of high-dose arm participants attending at least 8 of the 12 sessions. Secondary objectives were to compare high vs. low dose intervention effects on 6-month changes in fruit/vegetable servings per day (24-hour dietary recall), MVPA minutes per week (accelerometry), and blood and stool biomarkers.Results: From December 2019 to January 2021, 74 women were accrued. On average, women were 57.9 years old, 4.8 years post-diagnosis, with body mass index of 29.1 kg/m2 . Most were nonHispanic white (89.2%), 51.4% were diagnosed at stage I, and 40.5% were on endocrine therapy. Questionnaire and biospecimen data collection at 6-months were completed for 93.2% and 83.8% of the sample, respectively. In the low-dose arm (n=36), 94.4% of participants attended the single class, while in the high-dose arm (n=38) 84.2% of participants attended at least 8 of the 12 sessions live or via video archived on the website (mean 9.4 sessions). On average over the 6-month intervention period, participants responded to 71.5% of the text messages, 73.0% wore their Fitbit device ≥50% of the time, and 77.0% accessed the study website. Mean vegetable intake increased by 1 serving per day among women in the high-dose arm and decreased slightly among women in the low-dose arm (P=0.03). Changes in fruit/vegetable intake and MVPA varied little by arm. Blood and stool biomarker analyses are ongoing. Conclusion: We successfully conducted a remotely-delivered diet and PA intervention for BC survivors with high accrual, adherence, and retention during the COVID-19 pandemic. Women in the high-dose arm increased vegetable intake relative to the low-dose arm. Future research will refine and test the intervention in a larger and more diverse study population.
ABSTRACT
Machine Learning is a key branch of Artificial Intelligence that concentrates on the development of computational algorithms by creating models. It has caught major attention in the technological domain due to its various applications in speech recognition, recommendation engines, computer vision, automated stock trading etc. The model's performance is dependent on the dataset provided and its accuracy can easily be enhanced by expanding the training dataset. Post Covid-19, it has been observed that phishing websites are appallingly on the rise, especially the phishing attacks. These attacks are caused by cybercriminals using PDF's, Microsoft office documents and other attachments via emails. This paper focusses on discussion and comparison of different machine learning algorithms that are capable of detecting phishing emails and websites. The experiments have shown that that MultinomialNB attains the highest efficiency of 98.06% for phishing email detection and Decision Tree Classifier offers the maximum efficiency of 95.41% for phishing website detection. © 2021 IEEE.
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Analysis of irregularities in Covid-19 data could open a new window to learn more about the unprecedented problems of the current global pandemic. Of many, radiographs and clinical records are reliable sources for viral infection investigation and treatment planning. Clinical records help track the Covid-19 pandemic. In this paper, we present a Spike Neural Network (SNN) with supervised synaptic learning to detect abnormalities in Chest X-rays (CXRs) In other words, the proposed SNN can distinguish Covid-19 positive cases from healthy ones. In our decision-making procedure, we introduce clinical practice so Explainable AI (XAI) is possible to carry out. In addition, Support Vector Machine (SVM) with local interpretable model-agnostic explanation (LIME) provides reliable analysis of abnormalities in Covid-19 clinical data. © 2021 IEEE.
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Human history is observing a very strange time fighting an invisible enemy;the novel COVID-19 is the greatest challenge to humankind since the Second World War. The current outbreak of COVID-19 coronavirus infection among humans in Wuhan (China) and its spreading around the globe is heavily impacting global health and mental health. Novel coronavirus (n-CoV) is a generic name given to severe acute respiratory syndrome coronavirus 2(SARs-CoV-2). It has rapidly spread around the world posing enormous mental, social, economic, and environmental challenges to the entire human population. This paper evolved from an overview of the coronavirus and its effect on public health and economics. The main focus of this paper is to survey the various species and types of COVs. The overall statistics of the count around the world and an inclusive survey of its impact on society is being discussed in this paper. In this paper, the linear regression analysis of different vaccines commissioned around the world in COVID-19 and manifold updated information across India has been analyzed in a statistical approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Favipiravir and remdesivir are investigational drugs for coronavirus disease 2019 that is caused by severe acute respiratory syndrome coronavirus 2. The active forms of these drugs are reported to target and inhibit viral RNA dependent RNA polymerase, which is derived from 3-chymotrypsin like protease, a viral replicase enzyme. The present in silico study explores the comparative efficacy of these drugs to inhibit 3-chymotrypsin like protease and RNA dependent RNA polymerase, to plan therapeutic options for patients based on their disease severity. Active favipiravir and remdesivir molecules bind to 3-chymotrypsin like protease with energies of 6.18 and -6.52 kcal/mol in contrast to -5.62 and -3.91 kcal/mol for RNA dependent RNA polymerase. Further, hydrophobic interactions and salt bridge formations cement drug bindings with 3-chymotrypsin like protease, but not with RNA dependent RNA polymerase. Molecular dynamic simulation experiments, performed under certain experimental constraints reveal that the root mean square flexibilities of active residues in drug complexes with 3-chymotrypsin like protease are lower than in free 3-chymotrypsin like protease making the former more stable than the latter because of their rigidity and stabilities. Both drugs may hence serve as good therapeutic options for early stages of coronavirus disease 2019. However, more severe symptoms may be treated better with favipiravir due to its better binding with RNA dependent RNA polymerase, as compared to remdesivir. The "one drug does not fit all" concept is true for coronavirus disease 2019 as it is being currently realized by clinicians all around the world. Hence precise knowledge about critical interactions of these drugs with the viral enzymes will help medicos make vital therapeutic decisions on interventional options for patients who report to hospitals without over symptoms or with varying degrees of disease severity.
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Aim: The aim of our study was to assay pre and post covid-19 impact on school students in Chennai and their psychological impact of online learning on them. Material and methods: 500 school students were surveyed for their Post COVID and Pre COVID experience in their academic life. We calculated samples by using statistical packages for social science (SPSS). Result: The study highlights students performed and enjoyed organized learning experience in the pre COVID-19 time to post COVID lockdown. Conclusion: Although the most data were statistically insignificant (P>0.05), based on the number of responses for given conditions in the survey, we were able to infer that post COVID-19 was more stressful and had a greater impact on school students (Chi square, Correlation).
ABSTRACT
Background: Drug development strategies for treating COVID-19 focus on actives that either physically block angiotensin-converting enzyme-2 (ACE-2) receptors (viral entry point), or those, which inactivate viral proteases like 3CLpro or RdRp, inside the infected host cells. Objectives: The objective of the present study is to virtually screen phytochemicals for both these purposes. Methods: Molecular docking, molecular dynamic simulation (MDS) and multiple sequence alignment were employed. Results: All the screened phytochemical actives showed negative binding energies with their respective targets, attesting good complex stabilities. Among each set of ten actives, for blocking ACE-2 receptors and for inactivation of 3CLpro and RdRp, Dichamanetin-ACE-2, Glabrene-3CLpro and Naringenin-RdRp complexes were most stable, with binding energies of -9.8, -9.11 and -7.7 Kcal/mol respectively. MDS studies of these representative actives and their complexes, also attested to complex stabilities. Multiple sequence alignment analysis of nine significant amino acid residues of the Homo sapiens ACE-2 receptor, with nine different species, showed conservation of several residues. Conclusion: A set of phytochemicals actives can block ACE-2 receptors and prevent the entry of SARS-CoV-2 into host endothelial cells. Two other sets of actives can inactivate viral 3CLpro and RdRp enzymes and prevent replication of SARS-CoV-2 inside host cells. They all can hence be further explored for the control of COVID-19.
ABSTRACT
In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQIS-Net model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N-connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state-of-the-art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening. CCBY
ABSTRACT
Misinformation, in most cases, is the reconfigured content using basic tools. Fake information related to casualties, infections, contacts, lockdowns, investments, exam schedules, and immigration, leads to confusion, fears, phobophobia, discrimination, harassment, physical injuries, deaths, financial damages, reputational losses, and many more long-lasting side effects. Objective: The aim of this article is to provide an overview of the many ways in which misinformation and information leakage related to COVID-19 can influence the stakeholders, such that it gives policymakers and citizens a greater understanding of both direct and indirect risks and harms when assessing the challenges their countries are facing. Methods: An extensive literature review was done on the prevalence of the COVID-19 related misinformation and its associated significant psychological, reputational, physical, and societal implications on Indians. The novel and possible approaches to fight against the misinformation are described. © 2020 Bentham Science Publishers.