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In computer system keyboard is the most prominent input medium of all time. But lately, human community is living in an era of global pandemic being afraid of suffering from Coronavirus (Covid-19) and hence each and every person avoids touching anything. This is because of the fear of contracting this contagious virus and their mutants. So, to mitigate this issue, we present a method "webcam based virtual keyboard interface"to interact with a computer system. The code of this method is written using pre-built modules like OpenCV, MediaPipe, PyVDA, Win32API, etc. and Python 3.9. This approach uses matching the index finger and middle finger on the specific key. After that the virtual desktop switching mechanism is done by PyVDA. The PyttSX3 library plays the sound whenever any key is pressed or when a desktop switch is initiated, corresponding to the key pressed or the desktop switched. In this approach no additional hardware device other than the webcam, that is already available in the system, is required. This approach is also useful for those persons who wants the access the system, even when their hands are dirty. © 2022 IEEE.
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Deep learning (DL) is a very powerful computational tool for various applications in scientific and industrial research which can be real-time implemented for societal benefits. Several factors impact the development of optimized DL models for better prediction including the amount of quality sample data, domain-specific knowledge, and the architecture of the model for extraction of the useful features/patterns from the data. The present chapter demonstrates the state-of-the-art DL methodologies used by the researchers from different laboratories under the Council of Scientific and Industrial Research (CSIR), India to solve important research activities across several sectors like Medical, Healthcare, Agriculture, Energy, etc. The Convolutional Neural Network (CNN) techniques are utilized for Tumor diagnosis, classifying molecular subtypes of glioma tissues, and predicting driver gene mutations in glioma. Similarly, the Long short-term memory (LSTM) model is applied for the assessment of crop production, and transfer learning is used for the classification of tea leaves. Further, the ensemble LSTM methodology is implemented for short-term prediction of wind speed to enhance the renewable energy sectors. Finally, the multivariate LSTM models were developed by integrating the weather parameters for the prediction of covid-19 spread over different states in India which is an input for policy planning and supply chain management during the pandemic time. All the use cases are being validated and the results are quite satisfying and provide confidence for the real-time application of DL for scientific and industrial research and societal benefit to the common people. © 2023 Elsevier B.V.
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Background: Disparities in cancer screening have been well documented during the Covid-19 pandemic. However, there are limited patient-reported data describing the prevalence and drivers of patient hesitancy towards cancer screening and willingness to resume screening. As health systems continue to experience pandemic-related capacity strain, there is an urgent need for innovative models of re-engaging patients in preventive screening. To address this issue, we developed a medical student-led, high-touch outreach model to re-engage primary care patients at Brookside Community Health Center in cancer screening. Methods: We iteratively optimized semi-structured call scripts and surveys in English and Spanish to contact patients overdue for mammography screening. Student callers included medical and pre-medical students with native Spanish fluency. Using the call script, students identified patient-reported barriers and facilitated mammogram scheduling for consenting patients. For consenting patients, student callers placed a telephone encounter with a pended screening mammogram order in the electronic medical record. PCP confirmation of the order triggered outreach by the radiology department for mammogram scheduling. Patients also received reminder calls from students the week of their appointment. Primary outcomes include screening consent rates, mammogram scheduling and completion rates, and screening results. Patient survey responses were securely recorded using the REDCap survey platform. Results: 198 patients were eligible for the intervention. 60% are primarily Spanish-speaking and 81% are insured by Medicaid. 145 patients (73%) have successfully been contacted, of which 129 (89%) consented for mammogram screening. 74 (57%) of the consenting patients have scheduled their mammogram and 38 (29%) have completed their mammogram. 36% of consenting Spanish-speaking patients with active mammogram orders did not have a mammogram scheduled, compared to 9% of consenting English-speaking. To date, 6 patients had abnormal mammograms requiring subsequent diagnostic imaging, and 1 patient was diagnosed with ductal carcinoma in situ requiring oncologic care. Qualitative analysis of patient surveys found that primary barriers to screening included factors associated with the Covid-19 pandemic (32.9% of contacted patients), lack of awareness of overdue status (25.9%) and patient unavailability (e.g. outside of country) (20%). Conclusions: In this single-center quality improvement study, we found that patients had a high willingness to engage in cancer screening during the pandemic and that trainees can play a vital role in re-engaging patients in preventative care. The disparity between Spanish and English-speaking patients' ability to schedule a mammogram after the consent process suggests that patients with limited English proficiency face additional challenges in accessing screenings.
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STATEMENT OF PROBLEM/QUESTION: The COVID-19 pandemic has caused marked declines in cancer screenings and exacerbated preexisting disparities in cancer screening among vulnerable patient populations. DESCRIPTION OF PROGRAM/INTERVENTION: Despite the availability of robust quantitative data reporting disparities in cancer screening during the COVID-19 pandemic, there is a dearth of patient-reported data available describing prevalence and drivers of patient hesitancy towards cancer screening and patient willingness to resume cancer screening. Additionally, as health systems continue to experience pandemic-related bandwidth strain, there is an urgent need to develop innovative models of re-engaging patients in preventive screening that can successfully be implemented in the current healthcare environment. To address this issue, we developed a medical student-led, high- touch outreach model to re-engage primary care patients of the Brookside Community Health Center (BCHC) in cancer screening. We iteratively optimized semi-structured call scripts and surveys in English and Spanish to contact patients overdue for mammography screening. Student callers consisted of medical students and premedical students with native Spanish fluency. Call script language allows students to identify patient-reported barriers and facilitates re-scheduling of mammograms for consenting patients. For consenting patients, student callers input a telephone encounter with a pended screening mammogram order in the electronic medical record;the note is then routed to the patient's PCP for signing. Patients additionally receive reminder calls from students the week of their mammography appointment. MEASURES OF SUCCESS: Primary outcomes include screening consent rates, rates of mammogram scheduling and completion, and screening results. Patient response to survey prompts and student call summaries were securely recorded and analyzed utilizing the REDCap survey platform. FINDINGS TO DATE: 198 patients eligible for the intervention have been identified, of which 60% are primarily Spanish-speaking and 81% are enrolled in MassHealth (MA Medicaid). 145 patients (73%) have successfully been contacted, of which 129 (89%) consented for mammogram screening. 74 (57%) of the consenting patients have scheduled their mammogram, and 38 (29%) have completed their mammogram. Of note, 6 patients had abnormal mammograms requiring subsequent diagnostic imaging, and one patient was diagnosed with ductal carcinoma in situ requiring establishment of oncologic care. A preliminary qualitative analysis of patient surveys has found that primary barriers to screening included factors associated with the COVID-19 pandemic, lack of awareness of overdue status, and patient unavailiability (e.g. temporarily out of the country), and miscommunication between patients and the clinic. KEY LESSONS FOR DISSEMINATION: In this single-center quality improvement study, we found willingness to engage in cancer screening during the pandemic remains high and trainees can play a vital role in mitigating screening disparities during the pandemic.
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The COVID-19 pandemic also known as the Corona Virus worldwide epidemic is contemplate as the transcendent critical global health disaster in the world. Pneumonia, acute respiratory syndrome, and even death are the severity of this virus. We are living in a situation where Covid infection cases can be increased unexpectedly anytime if we do not follow the advisory of World Health organization (WHO). The majority of people who are infected with the virus has experienced mild to moderate fever. This virus spread rapidly in public places such as hospitals, metro station, railway station, malls etc. In such crowded areas, the chances of virus spread is high and we can prevent this by social distancing and measuring the temperature of the every individual without using human interference. In our idea we have introduced a fully automatic temperature detection system which would energized by piezoelectric generator. We have also implemented an automatic door opening system in which the door of a particular place will remain closed if temperature is above the preset value. The opening and closing of door is done through the piezoelectric generated power. © 2022 IEEE.
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Covid-19, the most devastating pandemics of human history, has adversely affected education fraternity. This unprecedented and sudden outbreak of pandemic has forced the education sector to go through complete makeover overnight. The objective of the present paper was to analysis the strengths, weaknesses, opportunities, and threats of online teaching of physical education and sports sciences during covid-19. Secondary data available online was utilized to complete the SWOT analysis. The findings of the study revealed that innovative audio-visual aids and tools employed by teachers, flexibility in timing and collaborative learning were recorded as strengths. However technical difficulties in term of usages of devices and software's, isolation and anxiety suffered by learner's were considered weaknesses. Lack of infrastructure and devices, network speed and connectivity, Digital illiteracy and divide and cost of technology were regarded as threats. Government initiative in this direction opened doors for many opportunities like creation of innovative program, tools, and technology. It was recommended that despite many strengths and opportunities a detailed and comprehensive online program and operational policy should be implemented by the government to nullify the grassroot problems. This work will help government, industry, and academia to prepare a better future road map for model online education. © 2021 IEEE.
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India is one of the world’s worst affected countries due to COVID-19 pandemic. The world is struggling to fight against centuries pandemic. Globally governments have been imposed lockdown and restrictions to control situation and minimize spread of infection. Social media was found the most practical and efficient medium to share information and opinions about pandemic. At time of social distancing, social media helped people to share their feelings and find support. Same time overuse of social media platform created panic and misinformation across countries. People sharing unconfirmed information about covid pandemic and governments were found it difficult to handle. © 2022, MedSci Publications. All rights reserved.
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Introduction: Severe Acute Respiratory Syndrome-Coronavirus (SARS-CoV-2) infection, also known as Coronavirus Disease-2019 (COVID-19) is the global pandemic, first described in Wuhan city of China in December of 2019. Its diagnosis depends upon real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR). On chest Computerised Tomography (CT), it is almost similar to other viral pneumonia with extensive parenchymal involvement. Semiquantitative scores depicting this extensiveness of involvement could correlate with disease severity, laboratory parameters, mortality, like Intensive Care Unit (ICU) admission, requirements of ventilatory support and longer hospital stay. Aim: To define role of chest CT score in determining disease severity, predicting poor prognosis and mortality of COVID-19 pneumonia in short-term follow-up. Materials and Methods: This prospective study enrolled all admitted real-time RT-PCR positive patients for COVID-19 at All India Institute of Medical Sciences, Rishikesh, India between 15th April and 31st May 2021. All patients were assigned semiquantitative CT scores based on the extent of lung parenchymal involvement of 20 lung regions in chest CT. Clinical severity was matched with chest CT scoring and laboratory findings. Survival curves along with univariate and multivariate analysis were applied to define the role of CT scoring in predicting short term prognosis. Results: Total 547 subjects were included in the study, of which the chest CT score showed a significant association with clinical seventies (p-value <0.001). CT score were correlating significantly with increased serum C-Reactive Protein (CRP) (p-value=0.001) and D-dimer (p-value=0.01), and decreased lymphocyte count (p-value=0.003). A CT score >= 31 was found to be associated with an increased risk of mortality in both univariate and multivariate analysis {Odd Ratio (OR)=276.8;95% Confidence Interval (CI). 45.21-1695.43;p-value <0.001}. Conclusion: Chest CT score can be imaging measure of disease severity and predict a higher probability of mortality in score >= 31. It can also predict other defined variables of short-term prognosis. So, it has an advantage in speedy diagnostic workflow of symptomatic cases, timely referral of patients to higher centre, and better management of critical care resources.
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The beginning of Coronavirus spread in humans started during December of 2019 in Wuhan, China leading to its extension throughout the world by March 2020. INDIA is the third-largest infected country in the world and the infection is increasing exponentially day by day. There needs to be a full-stack algorithm to Detect, Predict and identify the spread of COVID-19, which will play a vital role in minimizing the fatalities. Already multiple algorithms like Support Vector Regression (SVR) Polynomial Regression (PR) Deep Learning regression models are used widely for predicting the COVID-19 spread. These algorithms are based on Neural Networks and work efficiently. They also do have limitations related to the time taken for identification, detection, and prediction at the early stage of the spread, as they lack the necessary feature. The paper proposes a total idiot proof model dependent on AI to do early identification of the COVID-19 spread. The proposed half breed model depends on novel neglected calculations utilized in Covid-19 examination. The exploratory outcome on the plague information of a few normal regions and urban communities in India shows that 25% of the people with Covid-19 have a higher disease rate inside the single day after they were tainted. The proposed AI-based cross breed model can fundamentally decrease the mistakes of the forecast results and predicts the pattern of the pestilence. © 2021 IEEE.
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COVID-19 has impacted the lifestyle worldwide. Health, economy, daily routines, work culture, research, mode of communications etc. all have been adversely affected and a new normal has been defined. Until an effective and reliable vaccine is developed, social distancing is one of the key solutions to prevent the virus from affecting the community as lockdown is never a permanent solution to this problem. In order to ensure proper social distancing at any organization, a system is developed that is able to detect whether the people are following social distancing norms by using the proposed 'Distancia-the new normal Algorithm'. The system is designed such that it gives an alert whenever the social distancing norms are violated. It can be deployed with a CCTV camera, so that the live footage fetched by the CCTV can be directly used by the system for checking that the social distancing is being followed or not. Any organization regardless of the number of employees can use this system. Existing mobile applications designed for this purpose cannot be used at schools, educational institutes or any other organization where the use of mobile phones is prohibited. Also, since the effect of rain is not considered in any of the existing system, therefore the present applications can only be deployed at limited places where there is no hindrance caused by rain/snow etc. Thus, the proposed system can prove to be of great importance for organizations where keeping a check of the social distancing is difficult. © 2021 IEEE.
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The epidemic COVID-19 has profoundly influenced people's wellness worldwide and the number of fatalities from diseases continues to increase world-wide. Despite technology's remarkable success in our daily lives, notably in ML and DL, AI also helped humanity fight the grueling COVID-19 war. DL is only one approach of ensuring that potential data-driven technologies can help humankind manage COVID-19. Big data and artificial intelligence are used to leverage exceptional efforts to combat the COVID-19 pandemic crisis. In some prior disease outbreaks, various AI offshoots were deployed. AI was applied in the identification of disease clusters, case monitoring, future outbreak predictions, mortality risk, and diagnosis of COVID-19, resource allocation illness management, training facilitation, record maintaining and design identification for the investigation of the trend towards the illness. AI Machine learning can help to find out the strategies to prevent the Corona virus. This paper presents a polynomial based linear regression model to predict the future cases according to the current situation using data of last few months, showing the output on the graph. The paper also discusses the applications of AI Machine learning in Corona virus pandemic like forecasting infection rate, diagnose with images comprehensively and will also discuss the role of Machine learning in facilitating the development of vaccine as well. © 2021 IEEE.
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Aims Suicide accounts for 2.2% of all years of life lost worldwide. We aimed to establish whether infectious epidemics are associated with any changes in the incidence of suicide or the period prevalence of self-harm, or thoughts of suicide or self-harm, with a secondary objective of establishing the frequency of these outcomes. Methods In this systematic review and meta-analysis, MEDLINE, Embase, PsycINFO and AMED were searched from inception to 9 September 2020. Studies of infectious epidemics reporting outcomes of (a) death by suicide, (b) self-harm or (c) thoughts of suicide or self-harm were identified. A random-effects model meta-analysis for the period prevalence of thoughts of suicide or self-harm was conducted. Results In total, 1354 studies were screened with 57 meeting eligibility criteria, of which 7 described death by suicide, 9 by self-harm, and 45 thoughts of suicide or self-harm. The observation period ranged from 1910 to 2020 and included epidemics of Spanish Flu, severe acute respiratory syndrome, human monkeypox, Ebola virus disease and coronavirus disease 2019 (COVID-19). Regarding death by suicide, data with a clear longitudinal comparison group were available for only two epidemics: SARS in Hong Kong, finding an increase in suicides among the elderly, and COVID-19 in Japan, finding no change in suicides among children and adolescents. In terms of self-harm, five studies examined emergency department attendances in epidemic and non-epidemic periods, of which four found no difference and one showed a reduction during the epidemic. In studies of thoughts of suicide or self-harm, one large survey showed a substantial increase in period prevalence compared to non-epidemic periods, but smaller studies showed no difference. As a secondary objective, a meta-analysis of thoughts of suicide and self-harm found that the pooled prevalence was 8.0% overall (95% confidence interval (CI) 5.2-12.0%;14 820 of 99 238 cases in 24 studies) over a time period of between seven days and six months. The quality assessment found 42 studies were of low quality, nine of moderate quality and six of high quality. Conclusions There is little robust evidence on the association of infectious epidemics with suicide, self-harm and thoughts of suicide or self-harm. There was an increase in suicides among the elderly in Hong Kong during SARS and no change in suicides among young people in Japan during COVID-19, but it is unclear how far these findings may be generalised. The development of up-to-date self-harm and suicide statistics to monitor the effect of the current pandemic is an urgent priority. © The Author(s), 2021. Published by Cambridge University Press.
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INTRODUCTION: Coronavirus disease 2019 (COVID-19) is associated with a hypercoagulable state in adults with significant mortality attributable to thrombotic complications. We report on the coagulation profiles of children admitted to the Intensive Care Unit with coronavirus related illness. METHODS: We describe a single-center retrospective cohort of 22 children admitted to the pediatric intensive care unit with SARS-CoV2 related illness. All children were admitted with PCR or antibody positive SARS-CoV2 related illness, or if antibody negative met CDC criteria for MIS-C including close family contact with COVID-19 positive patient. Thromboelastography was performed on all patients in addition to routine hematologic assays. RESULTS: Majority of patients (55%) were female, with median age of 6 years (IQR 2.3,14), with predominantly hispanic (59%) and black (27%) race/ethnicity. Three children had preexisting comorbidities. The most common clinical presentation was multisystem inflammatory syndrome (77 %). Two children, both adolescent females presented with extensive thrombotic complications- one with deep venous thrombosis and gangrene of leg, one with multiple pulmonary emboli. One-third (36%) of children had abnormal values on thromboelastography (TEG). Hypercoagulability on TEG was the most frequent finding, characterized by acceleration of clot formation and increase in maximal clot amplitude. Most children had minimal to no fibrinolysis on TEG suggestive of impaired fibrinolysis. Elevation in D-Dimer, IL-6 level, ferritin and CRP was universal, and 50% had associated hyperfibrinogenemia (>400 mg/dL). Platelet count, prothrombin time and activated thromboplastin values were normal for all children except the two who presented with thrombotic complications. CONCLUSIONS: We report abnormal coagulation profiles in one-third of critically ill children with coronavirus related illness detected by thromboelastography (TEG). Routine coagulation variables were within normal range for most patients, while TEG uncovered a pattern of accelerated clot formation and increased clot strength. Our findings suggest significant inflammation associated with a distinct hypercoagulable profile in a subset of children with SARSCoV2 related illness. Viscoelastic tests may be useful to characterize these abnormalities.
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Global research team has announced that the health a management system at world level is in fear from CoV-19. Various statistical analysis has been done to check the preparedness to fight against CoV-19. Recent government responses of the different countries are also taken into the consideration while working for CoV-19 handling. Demographic trends are also added to add further content to potential impact of CoV-19 on healthcare services and system. This pandemic has raised a significant challenge to the economy of the different countries. Availability of beds are calculated on Per thousand people in different countries. Few of the countries analysis like Australia is having 2.6 beds per thousand people, while United Kingdom America is having 2.5 beds preparation over 1000 people. Per capita health spending in UK is marginally below the median. Hospital have been urged by government of different countries to postpone their surgeries and other treatments to provide the proper hospitality to cov-19 patients. India is at 145th place among 195 countries in healthcare access and Quality Index (HAQ)[1]. In this paper we have proposed a machine Learning model to predict the number of beds required as Cov-19 cases are increasing. Our Model Predicts the requirement for beds with 95% accuracy and acceptable p-value. © 2020 IEEE.
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In the times of covid-19, when fear and panic have dramatically divided the society, pushing the vulnerable populations further to the margins;the pandemic response of the Van Suraksha Samiti became a lifeline for the subaltern in Jharkhand. © 2021 Taylor & Francis Group, LLC.
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Coronaviruses are responsible for a developing budgetary, human and fatality trouble, as the causative factor of infections, for example, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has been well recognized that SARS-CoV-2 may survive under severe atmosphere circumstances. Hence, efficient containment approaches, for example, sanitizing, are crucial. Commonly, living compounds contribute a substance of chemical heterogeneity, with antiviral movement, and therefore can have efficacy as therapeutic tools toward coronavirus diseases. Here, in this review article, we have described the antimicrobial-based materials, which can be used to inhibit the spreading of the COVID-19. We have categorized these materials in three sections;(i) antimicrobial wall paint, (ii) antimicrobial papers and (iii) antimicrobial materials surface coating to be utilized as the antimicrobial-based materials for controlling the COVID-19. In the last section, we have given the concluding remarks with prospects in this area. © 2020, Tech Science Press. All rights reserved.