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Human civilization is now facing the biggest pandemic of coronavirus disease 2019 (COVID-19) of this century caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Absence of any suitable vaccines makes this virus much more detrimental;various drugs (antiviral drugs and drugs used for other diseases also) are repurposed to cope up with urgent requirements during this emergency period. This chapter will discuss about some of the important small-molecule drug activities and the mechanism of actions used to tackle COVID-19. © 2022 Elsevier Inc. All rights reserved.
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This work introduces a simple detector for SARS-CoV-2 (COVID-19) virus. The detector operates in a very simple mechanism. Peripheral circuits to represent the testing result are also simulated. The system can be designed and fabricated in a single integrated circuit (IC) chip. The response time analysis of the device shows the speed of detection of this device. This detector will be highly effective to detect the SARS-CoV-2 virus in the future. © 2022 IEEE.
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Comprehensibility is the most crucial factor for the design and evaluation of a sign. Evaluation of sign’s comprehensibility through appropriate method is of utmost necessity before its implementation to avoid the wrong interpretation and thereby devastating impact. Hence, a comprehensibility evaluation of a sign was attempted using the triangulation method to overcome intrinsic biases from a single method study. One important COVID-19 warning sign was shown to 50 volunteers (43 male 7 females, graduates, and non-OSH experts) who were employees of India’s leading manufacturing organization. Two different methods were used for the comprehensibility assessment of the given sign. One was in the form of a score, and the other was in the form of a short descriptive answer. Two OHS experts evaluated both types of responses. The threshold was tuned between 30 and 100%, and comprehensibility results were recorded accordingly. The given sign was found comprehensible to 40% of the volunteers in Method-1 and 48% in Method-2 when comprehensibility was judged based on the gold standard, i.e., 60% (score = 0.6). The findings of both methods were found to be almost similar and effective in evaluating comprehensibility. The triangulation using two different methods produced consistent findings and revealed high positive correlation of data between two methods (Pearson r = 0.86). Both collected data and methods were thus validated and qualified for the generalization of the observed result. Hence, researchers became confident about the results of the sign’s comprehensibility, although the sign was found less comprehensible, needing further research and redesign. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Background: Autoimmune disease in adults, among spectrum of complications of COVID 19 is rare. Case Study: Both the cases were never smokers and had type 2 diabetes mellitus, hypertension and hypothyroidism with history of severe COVID 19 eleven months back in case I and seven months back in case II. Case I is 36 year old female, had off and on cough and fever (with spikes of 102o F) for one month along with loss of appetite and weight. CECT thorax showed multifocal GGOs and nodules and CT guided biopsy showed epitheliod granulomas. Fever had not responded to course of ATT. Monteux test showed no induration and BAL was negative for CBNAAT, fungal smear and culture and malignant cytology for both patients. PET CT showed metabolically active bilateral lung nodules. NCCT PNS was suggestive of pansinusitis. cANCA was positive. Case II is a 63 year male, had off and on dry cough and dyspnea off and on (grade 2 mMRC) for 2 months associated with loss of appetite and weight. Hb was 6 gm/ dl and indirect Coombs test was positive. USG whole abdomen showed coarse echotexture with multiple small nodular hypoechoic lesions in liver and spleen and splenomegaly. Serum ACE level was 95 mcg/ L. CECT thorax showed discrete areas of consolidation and ground glass opacity with perifissural and peribronchovascular nodules. TBLB showed non necrotizing granulomatous inflammation. Diagnosis of post COVID ANCA associated vasculitis was made in case I and post COVID sarcoidosis in case II. Both patients responded to immunosuppression. Discussion: Literature shows evidence for similar pathogenesis and clinical-radiological aspects between the hyper-inflammatory diseases and Covid-19 which might explain SARS-CoV-2 for the development of a rapid autoimmune and/or autoinflammatory dysregulation. Host-pathogen interactions at different points of the viral life cycle seem to be important for explaining in part the heterogeneity of clinical pictures that characterize COVID-19. Conclusion: In this progressively increasing global COVID-19 pandemic, it remains necessary to investigate early to find the effects and interactions of various immunological and autoimmune diseases in patients with recent history of COVID-19 and further intervene.
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Background: COVID-19 and its treatment with corticosteroids and immunosuppressive therapy, mechanical ventilation, contaminated oxygen humidifier systems, prolonged hospital stay and uncontrolled diabetes mellitus increase the risk of fungal infections. Methodology: Inclusion criteria were patients with (i) recovery from moderate to severe COVID 19 & (ii) new onset cavitary lung lesions. Exclusion criteria were rhino orbito cerebral mucormycosis (ROCM). Results: Of all the 44 patients, (40, 90.9%) were males and never smoker (32, 72.7%). Mean age was 59.7 years. Comorbidities were DM (20, 45.4%) with HbA1c>5.4% in 16 (36.3%) and HTN (16, 36.3%). Mean ESR was 81.5 mm/1 hr & CRP was 112 mg/L. 22 (50%) underwent mechanical ventilation. Presenting symptoms were fever (34, 77.27%) and hemoptysis (28, 63.6%). Mean d-dimer was 1.93 g/ dL. Sputum yielded growth on fungal smear culture in 8 (18.18%). BAL galactomannan was raised in 26(59.1%) patients. 30 (68.2%) had cavitatory lesion in right lung with upper lobe involvement in (16, 53.3%). 36 (81.8%) patients underwent FOB. Most common endobronchial appearance was thick whitish mucoid secretions. 2 (4.5%) had endobronchial mass adherent to bronchial wall.BAL fungal culture yielded growth in 18(40.9%). TBLB yielded abnormal histopathology on 8(18.8%) patients. BAL showed mucormycosis in 14 (31.8%), MTB detected by CBNAAT in 8 (18.8%), aspergillosis in 8(18.8%) and candidiasis in 2(4.5%). During antifungal treatment, 12 (27.2%) died. Conclusion: After excluding ROCM, pulmonary mucormycosis followed by aspergillosis were the common fungal lung infections, in patients presenting to Pulmonary Medicine department of a tertiary care centre after recovery from COVID 19.
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Background: COVID-19 pandemic has been an unprecedented health crisis. Post COVID-19 lung sequelae comprise respiratory disease occurring after recovery from COVID-19. Objective: (1) To determine the baseline characteristics. (a) Inflammatory marker levels. (b) Spirometric values. (c) 6 minute walk distance. (d) Radiological parameters. (2) To assess the differences in above mentioned parameters, during follow up. Methods: All consecutive recovered patients of moderate to severe COVID-19 attending Post COVID-19 clinic were subjected to history taking of grade of dyspnea and preexisting co morbidities. At baseline visit, inflammatory markers (ESR, CRP, d-Dimer, Ferritin and LDH), Spirometry, 6MWD and HRCT thorax findings were determined. Spirometry and 6MWD were repeated in follow up visits (0, 3, 12, 24 weeks). Results: Of the 468 patients, 196 (41.9%) had moderate and 272 (58.1%) had severe disease. Ever smokers comprised 58 (16.2%) patients. 348 (74.3%) had comorbidities, most common being hypertension (192, 41%). The mean value of ESR, CRP, d-Dimer, LDH and Ferritin levels was higher in severe patient group, compared to moderate patient group (although statistically insignificant). Statistically significant decline was seen in ESR, CRP, ferritin and LDH levels in 1st follow up. Spirometric parameter, absolute FVC was higher in moderate group compared to severe group and statistically significant. Most common radiological finding was ground glass opacity (GGO), and treated with OCS. In OCS treatment arm, statistically significant increment in 6MWD was seen as compared to antifibrotic arm. Conclusion: Long recovery period should be expected in patients of moderate to severe COVID-19.
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Objective- The aim of this study was to assess whether major blood inflammatory parameters like neutrophil/lymphocyte ratio (NLR), interleukin-6 (IL-6) and high sensitivity C-reactive protein (hs-CRP) levels are associated with left ventricular remodeling parameters, New York heart association (NYHA) functional classes and pro-B- type natriuretic peptide (pro-BNP) levels in patients with idiopathic DCM. Methodology-59 patients with DCM were initially screened and categorized as idiopathic DCM and 22 were included for further analyses due to COVID-19 restrictions and positive cases were eliminated from the study. Biochemical assessment and echocardiographic investigations were done to assess cardiac structure and function. Results- There was a statistically significant correlation found between NLR and NYHA functional class (r=0.48, p<0.001), pro-BNP level (r=0.61, P<0.001) and left ventricular (LV) systolic parameters. Similar correlation was observed for IL-6 (with NYHA, r=0.40, P=0.011 and with pro-BNP, r=0.55, P=0.002). hs-CRP showed low to moderate correlation with the cardiac markers. NLR was also significantly higher in NYHA functional class III-IV patients (n=11) compared to NYHA class I-II (n=11), (3.1±0.5 vs 3.8±0.8, P=0.045). IL-6 showed similar significant difference between NYHA class I-II and III-IV (10.0±2.6 vs 20.0±11.2, P=0.045). NLR and IL-6 also found to be independent positive predictors of heart failure progression in NYHA class III-IV (P=0.019 and 0.015 respectively). Conclusion- Our findings clearly exhibit the efficacy of NLR and IL-6 in predicting severity of chronic heart failure in IDCM patients.
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During the pandemic, the supply chains got highly disrupted and faced a new challenge to sustain service. We have proposed a mathematical model for managing supply chains in a post pandemic situation, also coined as 'new normal'. We are trying to design a model and simulate different scenarios while optimizing the network to thrive and fulfill customer demand. The model has been supported with scenario analysis and illustrative examples a packaged drinking water supply chain. The objective is to minimize the supply chain operating cost with respect to the changes in capacity due to pandemic. The fill-rate has also been recorded as a performance matrix for the chain. Particle Swarm Optimization (PSO) has been used to optimize the objective function. This research will help supply chain practitioners and researchers to design networks and carry out study in risk management for pandemic or other similar outbreaks situations. © 2021 IEEE.
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Since the first virus was identified in the early last century, many kinds of different viruses have been discovered until now that can harm a human being. One of these is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or well known as coronavirus, which has pushed the entire world into a deadly pandemic. The pandemic has been affecting public health, employment, lifestyle, and the entire food system. To protect our house, workplace, and heavily populated areas such as markets and hospitals from being infected by the virus, it needs to be stopped in every possible way to be spread. Footwear is one of the potential sources of contamination and possible carrier of the virus, especially if it touches an infected place or someone who has already infected sneezes or coughs nearby. Since most footwear is made of leather, rubber, and plastic, the virus can live on these for many days at room temperature. Even footwear can be a breeding ground for bacteria and viruses as it comes in contact with dirt and germs more than anything else. In this paper, a smart device for disinfecting footwear has been proposed for crowded premises. The sensing device will automatically sense the visitor’s presence at the entrance and will disinfect his footwear by spraying disinfecting agent underneath the footwear or foot. This disinfecting station will allow visitors to disinfect their footwear without stopping and will ensure effecting sanitization of the entire sole even if the sole has deep flex grooves or high heels. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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COVID-19 pandemic is a worldwide task because of its excessive unfold and alarming mortality rate. The capability to forecast this scenario might permit the authorities to modify their plan and guidelines accordingly. Researchers worldwide are using different outbreak prediction models for COVID-19 to make informed decisions and implement applicable control measures. However, we should not use epidemiological models in India as they do not provide desired predictions as a vast country with a different socio-economic status and dynamically varying cases of infection in different locations. Thus, because of high variability and lack of evidence, epidemiological models have shown low reliability. This paper provides a comparative study of the time series, deep learning, and mathematical models to forecast the COVID-19 outbreak as an alternative to epidemiological models. It also includes a modified version of Levitt metrics in order to predict the peak. This research experiments with various methods having different structures and parameters to model the outbreak, based on the findings presented here and the complex virtue of the COVID-19 pandemic across India. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The evacuation of the population from flood-affected regions is a non-structural measure to mitigate flood hazards. Shelters used for this purpose usually accommodate a large number of flood evacuees for a temporary period. Floods during a pandemic result in a compound hazard. Evacuations under such situations are difficult to plan as social distancing is nearly impossible in the highly crowded shelters. This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter's stay. To the best of our knowledge, such a problem is yet to be explored in literature. Here we develop a simulation-optimization framework, where multiple objectives are handled with a max-min approach. The simulation model consists of an extended Susceptible-Exposed-Infectious-Recovered-Susceptible model. We apply the proposed model to the flood-prone Jagatsinghpur district in the state of Odisha, India. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. At the same time, this does not result in an uncontrolled number of new infections. The proposed approach can generalize to different regions and can provide a framework to stakeholders to manage conflicting objectives in disaster management planning and to handle compound hazards.
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Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from the COVID-19 spread. First, we propose a Bayesian framework to study the time-varying semiparametric AR(p) model for the count and then extend it to a more sophisticated time-varying INGARCH model. We calculate posterior contraction rates of the proposed Bayesian methods with respect to the average Hellinger metric. Our proposed structures of the models are amenable to Hamiltonian Monte Carlo (HMC) sampling for efficient computation. We substantiate our methods by simulations that show superiority compared to some of the existing methods that closely fit this setting. Finally, we analyze the daily time series data of newly confirmed cases in NYC to study the spread of COVID for three months.
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Following the first outbreak of COVID-19 in China, various continents became serious and aware to combat against it, though degraded dramatically preventing it, due to its unique transmission strategy. On March 8, 2020, Bangladesh confirmed its first cases of COVID-19 with three people being infected and the first death was reported on March 18, 2020, until June 29, 2020, the total number of infected people and deaths reached to 141,801 and 1783, respectively. Bangladesh has strengthened its efforts to improve the health care system's ability, including COVID-19 diagnosis to prevent the crisis, following discovery of the first 100 reported cases of COVID-19 at the start of April. Though, the government of Bangladesh had put in place preventive measures, the country has no remarkable legislative structures for combating COVID-19 in which Bangladesh, the South Asian low-income economic country, is under very precarious conditions and is at the forefront of the threat of disease that can spread to over the 160 million people. The aim of this article is to describe the current Bangladesh situation as well as the consequences in the country due to COVID-19 and to describe how people are confronted with this pandemic.
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COVID-19's impact has surpassed from personal and global health to our social life. In terms of digital presence, it is speculated that during pandemic, there has been a significant rise in cyberbullying. In this paper, we have examined the hypothesis of whether cyberbullying and reporting of such incidents have increased in recent times. To evaluate the speculations, we collected cyberbullying related public tweets (N = 454, 046) posted between January 1st, 2020 - June 7th, 2020. A simple visual frequentist analysis ignores serial correlation and does not depict changepoints as such. To address correlation and a relatively small number of time points, Bayesian estimation of the trends is proposed for the collected data via an autoregressive Poisson model. We show that this new Bayesian method detailed in this paper can clearly show the upward trend on cyberbullying-relatedtweets since mid-March 2020. However, this evidence itself does not signify a rise in cyberbullyingbut shows a correlation of the crisis with the discussion of such incidents by individuals. Our work emphasizes a critical issue of cyberbullying and how a global crisis impacts social media abuse and provides a trend analysis model that can be utilized for social media data analysis in general. © 2020 ACM.
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Objective: Handwashing is now considered as one of the best safety measures to prevent COVID-19 infection. The effect of excessive handwashing for health on OCD patients who are already having washing compulsion is not known. Furthermore, the fear of contamination of COVID-19 in patients who already have obsession of contamination is not known. This study aims to evaluate the effect of COVID-19 on OCD patients. Method: Phone interviews were done with 84 patients previously diagnosed with obsession of contamination and compulsive washing. Yale Brown Obsessive Compulsive Scale was used and the scores of the participants were compared to their prepandemic scores. Results: Only 5 patients (6%) had exacerbation of symptoms after the COVID-19 pandemic. Most of the patients did not report any deterioration of symptoms due to the pandemic. Conclusion: Handwashing protocol does not aggravate the washing compulsion of patients. Similarly, the fear of infection with COVID-19 does not increase their fear of contamination.