Prediction of Covid-19 disease using machine-learning-based models

According to Chinese health officials, almost 250 million people in China may have caught Covid-19 in the first 20 days of December. Due to the Covid-19 pandemic and its global spread, there is a significant impact on our health system and economy, causing many deaths and slowing down worldwide economic progress. The recent pandemic continues to challenge the health systems worldwide, including a life that realizes a massive increase in various medical resource demands and leads to a critical shortage of medical equipment. Therefore, physical and virtual analysis of day-to-day death, recovery cases, and new cases by accurately providing the training data are needed to predict threats before they are outspread. Machine learning algorithms in a real-life situation help the existing cases and predict the future instances of Covid-19. Providing accurate training data to the learning algorithm and mapping between the input and output class labels minimizes the prediction error. Polynomials are usually used in statistical analysis. Furthermore, using this statistical information, the prediction of upcoming cases is more straightforward using those same algorithms. These prediction models combine many features to predict the risk of infection being developed. With the help of prediction models, many areas can be strengthened beforehand to cut down risks and maintain the health of the citizens. Many predictions before the second wave of Covid-19 were realized to be accurate, and if we had worked on it, we would have decreased the fatality rate in India. In particular, nine standard forecasting models, such as linear regression (LR), polynomial regression (PR), support vector machine (SVM), Holt's linear, Holt-Winters, autoregressive (AR), moving average (MA), seasonal autoregressive integrated moving average (SARIMA), and autoregressive combined moving average (ARIMA), are used to forecast the alarming factors of Covid-19. The models make three predictions: the number of new cases, deaths, and recoveries over the next 10 days. To identify the principal features of the dataset, we first grouped different types of cases as per the date and plotted the distribution of active and closed cases. We calculated various valuable stats like mortality and recovery rates, growth factor, and doubling rate. Our results show that the ARIMA model gives the best possible outcomes on the dataset we used with the most minor root mean squared error of 23.24, followed by the SARIMA model, which offers somewhat close results to the AR model. It provides a root mean square error (RMSE) of 25.37. Holt's linear model does not have any considerable difference with a root mean square error of 27.36. Holt's linear model has a value very close to the moving average (MA) model, which results in the root mean square of 27.43. This research, like others, is also not free from any shortcomings. We used the 2019 datasets, which missed some features due to which models like Facebook Prophet did not predict results up to the mark;so we excluded those results in our outcomes. Also, the python package for the Prophet is a little non-functional to work on massive Covid-19 datasets appropriately. The period is better, where there is a need for more robust features in the datasets to support our framework. © 2023 River Publishers.

Food commerce upshots with their former locus in the economy with respect to the pandemic 2020 - a drawback or an aid in South Kolkata, West Bengal

Background: The Food Commerce industry has flourished massively during the past decade in South Kolkata in West Bengal, with new outlets opening every now and then, so much so that this region is known as 'Food Street'. Regardless of their scale of operations, each of these outlets had well established themselves, catering to their respective target markets and earning decent amount of revenue over the years. However, this growth suffered a setback owing to the origin of novel Coronavirus SARS-n-CoV-2. The growth rate declined to a great extent over the span of two years, with recent studies showing an overall stunted growth rate. Even though online marketing of these outlets and selling the food through delivery apps have aided the entrepreneurs, the cost to revenue ratio is not at par with that of the times before the pandemic hit. Overall, the pandemic has impacted the eateries in more way than initially imagined. Objectives: (a) To reveal the various problems and scenarios of managing food business during the Covid-19 pandemic in South Kolkata region;(b) To compare the present scenario of the food industry with how things were before prior to the pandemic to understand the nature of change during this time frame;and, (c) To describe the challenges and methods implemented by the food retail business entrepreneurs and managers of the randomly selected establishments to hold a steady business flow during the Covid-19 pandemic. Methodology: The study follows a descriptive research design. Therefore, the research will describe the characteristics of the sample under study. The food outlets of South Kolkata have been chosen as the study location. 100 respondents were selected. The respondents are those who consume food from these outlets such that they represent the wider target market of the 'Food Street'. Both Primary Data and Secondary Data were used. Primary Data was collected through sample survey. Random Sampling technique was used to choose the respondents. The study used quantitative data, therefore, only Quantitative analysis was performed. Results: The Research was able to depict the comparison between the present scenario and the situation prior to the pandemic. The study was able to reveal the challenges and problems that the food outlets had to suffer from. Also, the methods or strategies taken up by the entrepreneurs of these outlets to overcome the pandemic were discovered. 46% of the respondents opted for "Mobile Food Delivery" as their strategy to revive from losses. Conclusion: With COVID-19 having altered - and still in the process of altering - the definition of "normal" across the world, most industries are still scrambling to adjust. The effect on the restaurant industry has been particularly dramatic. With restaurants and pubs closed for sit-down service, many establishments are struggling to keep their heads above water. The food outlets located in South Kolkata shares the same fate and the research is able to highlight this effectively.

At the Crossroads of One Health: Lessons Learned from Foot and Mouth Disease in COVID-19 Pandemic

Foot-and-mouth disease (FMD) and Coronavirus Disease 2019 (COVID-19) are transboundary diseases caused by single-stranded positive-sense RNA viruses with similarities in genome replication and viral protein synthesis. In FMD, asymptomatic infection leads to carrier status and persistently infected animals that threaten the animals vaccinated with a trivalent inactivated whole virus vaccine. Similar information on COVID-19 is not yet available. As COVID-19 vaccination is introduced in January 2021 (since 16 January 2021 in India), its outcome can be assessed by the year-end;and while doing so, the experiences gained in the control of FMD in livestock worldwide can be applied, including monitoring of vaccination response, duration of immunity, level of herd immunity developed, and antigenic matching of the vaccine virus. Antigenic divergence of the virus is a major issue in FMD, and different geographical regions in the world use different virus strains in vaccine preparations to antigenically match circulating virus strains in respective regions for control of the disease. Non-synonymous mutations in the critical antigenic determinants of SARS-CoV-2 have been observed, and there is likely the existence/development of antigenic variants. Therefore, during the post-COVID-19 vaccination regime, it will be essential to monitor the suitability of the in-use vaccine strain region-wise from time to time, as there could be an eruption of isolated outbreaks in a country arising due to antigenic variation and variants. In the context of the present scenario of COVID-19 around the Globe and multiple ongoing efforts to develop suitable vaccine(s) to control the disease, it is a must to develop NSP-antibody (that differentiate infected from vaccinated) assays to differentiate infected from vaccinated individuals(DIVI;DIVA in veterinary epidemiology). The techniques used and experiences gained in ongoing FMD control programs in the endemic countries can be applied to COVID-19 control in a country;and finally, the Globe. After achieving the control of COVID-19, the aim would be to eradicate the virus, which will be tough even with vaccination, as the disease/infection may become endemic during the time to come. To achieve this, applying the principles of Progressive Control Pathway for Foot-and-Mouth Disease (PCP-FMD;FAO/OIE) to COVID-19 control will be beneficial in its control. The present review discusses the issue of control of COVID-19. © 2021 by the authors.

Robotic Process Automation - The Ineluctable Virtual Workforce in Various Business Sectors: Post Covid-19 Scenario

Covid – 19 is a sudden unexpected jolt on the functioning of every sector's day-to-day work and dragged everybody to find the best alternative for the automation of processes to enable more work from home. Robotic Process Automation (RPA) is one of such promising technologies which would help in the automation of mundane everyday tasks. With this technology, software robots would be deployed to perform the repetitive tasks done by humans in various sectors. This technology is also sometimes termed software robotics. In this paper, we have studied and discussed about the different tools used for developing software robots, traditional methods of completing the task without RPA being used and also how the task is automated after the use of software robots in various fields, viz., banking, e-commerce, educational institutions, human resources, health care and office. We have also discussed the pros and cons of automating the task using robotic process automation. In short, this paper will give an overview of the emerging technology – RPA. © 2023 Seventh Sense Research Group®

Self-Care: A Thematic Study of Self-Care Posts Amidst COVID-19 on Instagram

"Selfcare" is a popular hash tag on social media and an important topic with a plethora of attributes and angles attached to it. Self-care has become more crucial amidst the pandemic. Social media is the platform people utilize to gain perspective and discuss self-care. This study used a thematic approach to analyze 100 word-centric posts under #self care on Instagram uploaded between May - July 2020 and found five major themes. The findings indicate that the topic has been presented with multiple distinctions on social media;it allows the researcher to see and make sense of collective or shared meanings and experiences. Thematic Analysis has been used in previous research to understand various topics on social media platforms. Since there is a variety within this single hash tag, different people will relate to multiple themes according to their respective situations. They will help them provide fresh ideas to handle a situation. The paper also proves that the post educates the viewer and has numerous positive advantages with the consumption of these posts. © 2023 American Institute of Physics Inc.. All rights reserved.

Procalcitonin as a Predictive Marker in Covid- 19: A Systematic Review and Meta-analysis

Coronavirus disease 2019 has emerged as a global pandemic causing millions of critical cases and deaths. Early identification of at-risk patients is crucial for planning triage and treatment strategies. We performed this systematic review and meta-analysis to determine the pooled prognostic significance of procalcitonin in predicting mortality and severity in patients with COVID-19 using a robust methodology and clear clinical implications.We used Preferred Reporting Items for Systematic Reviews and MetaAnalyses and Cochrane Handbook for Systematic Reviews of Interventions guidelines. We included thirty-two prospective and retrospective cohort studies involving 13,154 patients. The diagnostic odds ratio of procalcitonin for predicting mortality were estimated to be 11 (95% CI: 7 to 17) with sensitivity, specificity, and summary area under the curve of 0.83 (95% CI: 0.70 to 0.91), 0.69 (95% CI: 0.58 to 0.79), and 0.83 (95% CI: 0.79 to 0.86) respectively. While for identifying severe cases of COVID-19, the odds ratio was 8.0 (95% CI 5.0 to 12.0) with sensitivity, specificity, and summary area under the curve of0.73 (95% CI 0.67 to 0.78), 0.74 (0.66 to 0.81), and 0.78 (95% CI 0.74 to 0.82) respectively.Procalcitonin has good discriminatory power for predicting mortality and disease severity in COVID-19 patients. Therefore, procalcitonin measurement may help identify potentially severe cases and thus decrease mortality by offering early aggressive treatment.

Flexible bronchoscopy in a critically ill-improving patient and operator safety

Flexible bronchoscopy is an indispensable diagnostic and therapeutic tool in respiratory critical care. In a critically ill hypoxemic patient, bronchoscopy is challenging due to interference with ventilation and oxygenation. During repeated disconnections, there is a risk of worsening gas exchange, cardiac arrythmias, hemodynamic instability and increased aerosol generation. As with COVID pandemic, prevention of transmission of infection in healthcare setting is important. This equally holds true for other airborne infections. We propose the use of a novel accessory - closed bronchoscopy device during bronchoscopy in ventilated patients, to reduce the procedure related risk to patient and health care workers. The bronchoscopy valve and sheath in close connection helps to prevent loss of volume, minimize disconnections and desaturation, prevent direct handling of soiled bronchoscope and reduces aerosol generation. This in turn reduces risk of introducing new infection in an airway during the procedure. Overall improved procedure safety, reduced time required without much handling difficulties. This requires scope-valve fit test. The disadvantage during therapeutic bronchoscopy where disconnections are necessary to clear blocked channels or remove large plugs can be managed at the valve level as shown in figure 1. This novel device will prove useful for reducing complications during flexible bronchoscopy in a critically ill.

Social Distance Alerting System Using Machine Learning

The increase into the Corona virus pandemic led to a higher death rate globally. The best way to prevent getting sick is to keep yourself physically or socially far. Our project provides an approach for physical isolation revealing using machine knowledge toward indicate the necessary space to be maintained to decrease the collision of the corona virus contagious widespread spread. By analyzing a videotape provide for from the camera, the detect apparatus be fashioned in the direction of notify individuals toward maintain a out of harm's way aloofness on or after one an additional. The open-source person recognition pretrained model, YOLO3 algorithm, was utilized to recognize people using the video frame from the camera as input. YOLO3 has the benefit of mortal a lot quicker than further algorithms, at a halt maintain exactness and meets the real-time requirements for person detection. In order to calculate distance from the 2D plane, the video frames are afterwards transformed into top-down views. Estimated distance between individuals and any non-compliant pair of individuals within the display is indicate by means of a red colour edge and stripe, the moderate distance is represented with orange colour and the safe distance is represented by green colour frame. The suggested technique was examined lying on a pre record videotape as well as on the live video feed of persons walking on the road. Additionally an alarm sound is provided to notify the persons. The outcome show that the planned strategy is ready toward sees the societal separation trial among many populaces withinthe videotape. © 2022 IEEE.

Abhigrith: Central Adoption System

Over the years, the number of orphans in India have been increasing and the adoptions taking place have not seen significant growth in them. The current system provided by the Central Adoption Resource Authority (also known as CARA) is inefficient and backward. Due to this, the parents get frustrated and give up on the idea of adopting a child. With the recent introduction of COVID-19 pandemic, the problem has become more relevant. There are also various other issues such as child trafficking, illegal adoption, child labor, etc. that have come into the picture due the lack of security in the existing system. To solve this problem, there is a need to develop a system that will replace the current parent-centric process to a child-centric process. The system will be at a centralized location so that it is accessible to all the stakeholder. The system will enable the adoption procedure which will be faster and more responsive. © 2022 IEEE.

A Real Time Monitoring System for Home Isolated COVID-19 Patients

During this Covid-19-time number of patients in hospital are increased. The covid-19 patients are mainly of two types one who has a serious condition and the other who has a mild covid-19 symptom. On the other way, the patients who have very serious conditions generally have all facilities, they have doctors around them and other medical staff also there to take care of the situation, but the patients who have not very serious conditions are generally isolated in their home, a problem with this home isolated patients are they do not consult with doctor day to day and what happens if patients condition become serious basically this paper is going to solve this two problem and also monitoring the patients while getting different data like a heartbeat, SpO2, body temperature, etc. along with that we used a location sharing and nearest hospitals identification. Here we design IoT, GSM/GPS-based system through which we send the patients' health data directly to the hospital and if the patients' conduction becomes serious then it sends an alert to the hospital along with the patient's location and it also sends the hospital location to the patients. © 2022 IEEE.

Exploring the transition challenges of first-year College of Business students in Fiji

Transition challenges (or Trans-C) are perceived as a combination of academic, institutional, socio-cultural and psychological factors which influence a student's ability to transition successfully in their first year at higher education institutions. This paper presents an exploratory study of first-year College of Business students enroled at the Fiji National University (FNU). Using a mixed method approach, the study is aimed at understanding the accounts of first-year students in transition, and that of FNU being a local academic institution mandated to support students facing difficulties. Quantitative data from an online questionnaire collected independent variables related to students' ability to transition successfully. Results showed that despite the establishment of transition hubs and other academic support services, about 30% of the total 183 respondents indicated transition difficulties in their first year. Qualitative data were collected using ‘Talanoa' method to account for students' transition experience. Talanoa or story telling is a culturally appropriate method of data collection in indigenous Pacific Island countries such as Fiji. This approach allows a rich and more open information exchange provided the researcher is embedded in local pedagogy. Several themes emerged from the analysis of the ‘talanoa' transcripts such as academic support, family income, online learning, University environment, and peer influence. Although these themes were consistent with international experience, it would be asymmetrical to address student transition challenges without capturing their needs and expectations. In the final analysis, this paper supports the implementation of a student-centric model if higher education institutions (HEI) were to be successful in integrating first-year students into academic life.

Knowledge Retrieval using Question Answering approach on Epidemiology data

The tried and tested way for effective Knowledge Retrieval is by posting questions and retrieving data from the huge information repository. In the recent past the prevalence of pandemics and the spread of COVID-19, has led people to rigorously question the various forms of epidemiology data available on different sources. In general, the amount of information gathered is proportionate to the questioning patterns by the knowledge seeker. Question answering (QA) system is useful during unexpected situations, especially during a pandemic. In this paper, we have proposed a Knowledge Retrieval Question Answering system (KRQA) for answering the queries of users related to COVID-19. The KRQA system is divided into two modules. The first module consists of preprocessing (tokenization, stemming, bag of words) of the question to produce a word vector. The second module involves building, training, and testing the data repository. Feedforward neural network is used to extract the most relevant answer from a repository of all possible answers. The volume and quality of information about the pandemic scenario around the world are increased at a tremendous rate. Hence our work focuses on effective knowledge retrieval using question and answering approach. Our experimental results are found to give better results based on Percentage closeness, precision, and recall parameters. KRQA has the novelty of retrieving more relevant answers with good quality. © 2022 IEEE.

Understanding Student Perceptions Around Mandatory Use of Smartphone Apps for COVID-19

In this short paper, we present our findings from a survey (n=178) conducted on a college campus to understand student perceptions of the deployment of CoVerified app to regulate COVID-19 spread on campus by limiting access to on-campus locations and events to only those with a "cleared"status. We expect our recommendations could help with future deployments of smartphone apps for managing spread of COVID-19, should there be new variants. Additionally, we also highlight our innovative pedagogy of bringing research into the classroom by incorporating the study data into a class project. © 2022 Owner/Author.

Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks.

The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

Supply Chain Performance Measurement Methodology for COVID-19 Disruption Recovery- Natural Gas Retailing in India

The coronavirus(COVID-19)pandemic disruption adversely impacted the Supply Chain for Natural Gas(NG) retailing, jeopardizing committed targets for thesocio-economic transition toa gas-based economyleading to inclusive growth in India. Furthermore, Omicron'srapid spread is a cause of global concern. This research aims to develop a conceptual Supply Chain Performance (SCP) measurement methodology for reducing recovery time post exogenous disruption. Exploratory research involving an integrative literature review identifies different approaches, frameworks, models, and techniques for Supply Chain PerformanceMeasurement(SCPM). Analysis reveals that the application of DMAIC improves the reliability of Supply Chain Management (SCM) functional processes during disruption recovery while synthesizing the Balanced Scorecard (BSC) with Triple Bottom Line(TBL) accounting approach provides a rigorous robust method to represents financial, social, and environmental performance goals. Accordingly, findings present a methodology with nine systematic steps to evaluate performance by City Gas Distribution(CGD)entities to accelerate NG retail consumption and speed up network expansion to meetthe growing clean energy demand of the urban populace. Additionally, anIndian model for transition to a gas-based economy is also presentedto distinguish competing goals to harmonize growth with environmental sustainability and ecological modernization. Though SCP is a mature concept, the literature on its methodology during disruption recovery for NG retailing is scanty. Hence, the proposed action-oriented SCPM methodology has practical implications for improving SCP, contributing uniquely to the retail SCM domain for NG. Its application will instigate industry practitioners to review Supply Chain Management (SCM) processes and strategies to improve service effectiveness and delivery efficiency.

Impact of Covid-19 Pandemic on Teaching and Training of Orthopaedic Resident in Medical College

Background: Many elective surgeries are getting postponed which directly affect the learning of surgical skill in resident. Various teaching activity such as OPD and bed side teaching also reduced because of decrease load and social distancing. Purpose of the study is to analyse the impact of this pandemic on orthopaedic education of resident in medical college. Material and Methods: A questionnaire was prepared on Google form for online survey. Total 74 residents responded, in which JR-1 (26), JR2 (24), JR-3 (19) and SR (5) participated. This study is an observational study in which we analysed the impact of this pandemic on orthopaedic teaching and training of resident in medical college. Result: In this pandemic clinical work in the form of OPD, IPD, no. of surgery (both elective and emergency) observed, assisted, independently performed significantly reduced. This directly affected the skill development of growing orthopaedic surgeon. Classroom teaching and bed side teaching were grossly affected. Some online platforms were used to teach the resident, but they are not as effective as conventional methods. Because of decrease clinical workload resident’s thesis and research work also affected which is an essential part of resident education. Conclusion: In summary, the results of this online survey on residents showed that there significant impact of the COVID-19 pandemic on teaching and training of orthopaedic resident in government medical colleges of central India.

New Education Policy: Contents and Sentiment Analysis

The year 2020 has been an extraordinary year for nations across the globe. In India, aside from Covid19, one of the significant changes that occurred was the advancement of the New Education Policy (NEP) 2020. Time by time, different boards of trustees have suggested expanding the allotment of the spending plan for training to 6% of the GDP, this has prompted the interest of specialists. This paper expects to recognize the worries and focal point of NEP 2020. The creators have used subjective information investigation strategies to comprehend basic areas of focal point of strategy records and PC helped subjective information examination programming to resolve the issues. The examination likewise utilizes information from Twitter. The information (strategy archives) was downloaded from the Ministry of Human Resource Development (MHRD) site. The paper recognizes three urgent parts of the arrangement - course, language, and understudies, for the advanced education area. This paper additionally examines the feeling investigation, connected with NEP 2020. It was found that the vast majority think about the strategy a positive and inviting advance.