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Favipiravir is an antiviral drug with significant and widespread antiviral action. Favipiravir was crucial in the contest against the COVID-19 pandemic because of how well it treated the SARS-CoV-2 virus. It is well known that contemporary pharmaceutical analysis establishes green, stability-indicating analytical procedures. The current study aimed to develop and assess UV-spectrophotometric (zero order, first order, area under the curve) and RP-HPLC methods for estimating favipiravir in its pharmaceutical dose form, comparing them using ANOVA and an in-vitro dissolution analysis. A green solvents composition of methanol, ethanol, and water (25:35:40 v/v/v) is used for analysis as a mobile phase and diluent. Method A is a simple zero-order spectrophotometric method for determining favipiravir at 236 nm, and the correlation coefficient in the linearity study was found to be 0.9962, LOD, and LOQ are 0.18 and 0.55 mg/mL. Method B is a first-order spectrophotometric method for determining favipiravir at 227 nm, and the correlation coefficient in the linearity study was found to be 0.9964, LOD, and LOQ are 0.64 and 1.96 mg/mL. Method C is an area under the curve spectrophotometric method for determining favipiravir at 230 to 243 nm, and the correlation coefficient in the linearity study was found to be 0.9986, LOD, and LOQ are 0.32 and 0.96 mg/mL. Method D is the RP-HPLC method for the determination of favipiravir at the retention time of 7.216 min, a flow rate of 0.80 mL/min, column temperature of 25 degrees C, at 236 nm, Isocratic mode, and the correlation coefficient in the linearity study was found to be 0.9996, LOD, and LOQ are 0.52 and 1.56 mg/mL. All developed methods demonstrated good repeatability and recovery with %RSD < 2. The proposed established methods were assessed using one-way ANOVA. It was revealed that the Fcalculated value was lower than the Ftabulated value, with no discernible variation in the assay results. Studies on stress degradation show that oxidation and acid degradation mostly impact favipiravir solutions. The Analytical Eco-scale verified that these methods are the greenest and most environmentally friendly, enabling the suggested approach to use an effective green analytical methodology to measure favipiravir extensively. Phosphate buffer (pH 6.4) was the best dissolution medium after analysis of the favipiravir dissolution study in several dissolution media.
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The CoVID-19 infections began rising worldwide during the initial weeks of March 2020, reacting to which the Government of India called for nationwide lockdown for ~ 3 weeks. The concentration of pollutants during the lockdown were compared with pollution levels recorded during the preceding year for the same time frame. A direct relationship was established between the high level of air pollutants (PM2.5, PM10, NO2 and SO2) and CoVID-19 infections being reported in the Indian cities. The correlation indicates that the air pollutants like PM2.5, PM10, NO2 and SO2 are aggravating the number of casualties due to the CoVID-19 infections. The transmission of the virus in the air is in the form of aerosols;and hence places which are highly polluted may see a proportionate rise in CoVID-19 cases The high-level exposure of PM2.5 over a long period is found to be significantly correlated with the mortality per unit confirmed CoVID-19 cases as compared to other air pollutant parameters like PM10, NO2 and SO2. © 2023, The Author(s), under exclusive licence to Korea Spatial Information Society.
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The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up. So we are working on COVID-19 dataset on Omicron variant to recognise the name entity from a given text. We collect the COVID related data from newspaper or from tweets. This article covered the name entity like COVID variant name, organization name and location name, vaccine name. It include tokenisation, POS tagging, Chunking, levelling, editing and for run the program. It will help us to recognise the name entity like where the COVID spread (location) most, which variant spread most (variant name), which vaccine has been given (vaccine name) from huge dataset. In this work, we have identified the names. If we assume unemployment, economic downfall, death, recovery, depression, as a topic we can identify the topic names also, and in which phase it occurred. © 2022 IEEE.
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Background COVID-19 as a trigger for A-beta+ ketosis-prone diabetes (KPD) [1,2] in previously normoglycemic individuals presenting with new-onset DKA, has been sparsely studied. Aim To study prospective changes in insulin secretion and insulin resistance in suspected A-beta+ KPD patients presenting with COVID-associated new-onset DKA. Method 22 previously non-diabetic, antibody-negative patients with new-onset DKA and RT-PCR positive COVID-19 (suspected A-beta + KPD), were followed up for one year. They were compared with 20 Type 1A and 18 Type 2 DM patients, with serial assessments (0,6 and 12 months) of insulin secretion rates (ISR) and multi-tissue insulin resistance (IR). 75-g OGTT with serial glucose, insulin and C-peptide estimation (0,15, 30,45, 60,90,120, 150 and 180 minutes) was used to derive IS, while hepatic and peripheral IR was calculated based on study by Ghani et al. [3]. Results At baseline, ISR in suspected KPD (n = 22) was significantly reduced but similar to Type 1A DM(p = 0.15). Serial ISR demonstrated complete recovery in 17 (77%) patients who became insulin independent at one-year follow-up (remission), while 5(23%) patients continued to require insulin (non-remission). KPD patients showed significant hepatic and peripheral IR at baseline compared to Type 1A DM (p < 0.05). The remission group (n = 17) showed significantly enhanced recovery of hepatic and peripheral insulin sensitivity at 6 and 12 months follow-up (all p < 0.01) compared to the non-remission (n = 5) group, with IR in the latter being comparable to Type 2 DM at follow-up (all p > 0.05). Younger age, lower BMI, initial severity of DKA and inflammation (IL-6 levels), along-with reduced 25-hydroxy-Vitamin-D levels were factors associated with poorer recovery of beta-cell secretion amongst the KPD patients. Conclusion This is the first prospective study to demonstrate progressive recovery of p-cell secretion in new-onset A-beta + KPD provoked by COVID-19 infection in Indian adults, with a distinctly different profile from Type 1A DM.Copyright © 2023 Elsevier B.V.
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Background: Covid-19 impacted not only people's lives but also slowed down the healthcare delivery system and supply chain leading to a global drug shortage.1 According to the Ministry of Statistics, India's growth in the year 2020 went down by 3.1% because of the pandemic, which impacted patient's capacity to continue with the expenditure related to chronic disease management. Rheumatoid arthritis (RA) for a patient comes with a out-of- pocket high cost long term immunosuppressive medicine and increased chances of secondary infections leads to non-adherence of patients. The current study is to observe the adherence to Janus Kinase (JAK) inhibitors in a hospital-based rheumatology service in Eastern India during the Covid-19 pandemic period. Method(s): Data of the patients enrolled physically and electronically under active follow-up in the Rheumatology Outpatient Department (OPD) of the hospital were analyzed.2 The patients with a confirmed diagnosis of RA, receiving JAK inhibitors for 6 months or more were included in the study from 21st March 2020 to 31st July 2020. A questionnaire was also administered to these patients to understand the impact of Covid-19 on the treatment of RA. Data related to demographic features, clinical, laboratory, drug history, and current treatment were collected and statistically analyzed. Result(s): Out of the total 42 patients (aged 38-76 years) who received JAK inhibitors, 24 (6 were COVID positive) were seen with the OPD during the Covid-19 pandemic. In our study, a higher proportion of patients with an annual income of INR 1M-1.5M had a 15% income decrement (Figure 1), though the patient adherence to JAK inhibitors was high compared to biologics, even in the patients who faced up to 25% reduction in annual income. Out of 24, only 4 patients stopped the treatment with JAK inhibitors due to the limited availability during the initial period of the lockdown. Overall patient adherence to JAK inhibitor treatment was 85% and was higher compared to the biologics (previous data). There was higher non-adherence in the biologic group at lower-income slabs (5-10 Lacs & 10-25 Lacs group) than in the higher income slabs, compared to JAK inhibitors inspite of better availability. Higher-income groups showed lower non-adherence in both groups. Conclusion(s): In the milieu of the Covid-19 pandemic, the treatment adherence in patients with RA was driven by the cost and availability of the medication amidst the pandemic. The association of injectable biologics with higher immunosuppression in patients perception during pandemic also affected the treatment adherence in patients. Thus it can be concluded that patient perception and availability were the main driving factor in adherence to RA therapy.
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Introduction: Worldwide there was recent outbreak of a novel Corona Virus infection i.e. SARS COV19. Although it involves primarily the pulmonary system other systems like Cardiovascular, renal, neurological, hematological systems are also significantly involved by SARS COV19 infection. Aim(s): In this study our aim was to analyze the pathophysiologic mechanism of hematological abnormalities in COVID-19 patients and its role in risk stratification, severity & prognosis of the disease. Material(s) and Method(s): In this study we have analyzed the clinical presentation and pathological laboratory results of hematological abnormalities retrospectively from previous records of COVID-19 patients admitted to our hospital. All the hematological parameters i.e. changes in Hb%, WBC Count, Platelet Count and Coagulation Profile parameters i.e. PT-INR, aPPT& D-dimer were analyzed and correlated with the disease severity and its prognosis. Statistical analysis was done be x2 test. Result(s): In our study the most common hematological abnormality was Lymphopenia followed by Leukocytosis and majority of the patient were >60 Yrs. age with male predominance. Conclusion(s): Among all the hematological abnormalities coagulation parameter D-dimer (elevated levels) are most significantly associated with disease severity. Among the other hematological abnormalities the most common abnormality was Lymphopenia which along with combined features of Anemia, Leukocytosis andNeutrophilia were also significantly associated with disease severity. So, monitoring & evaluation of hematological parameters could be a crucial step towards risk stratification & management of COVID-19 patients. Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.
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COVID-19 disease is caused by Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2). In most of the cases the patients present with typical symptoms of fever, cough, dyspnea, sore throat etc. The involvement of central nervous system by SARS-CoV-2 resulting in encephalopathy, encephalitis and neuropsychiatric symptoms such as anxiety, depression, panic attack and post traumatic symptoms have been described in the literature. But the clinical presentation of Psychosis as a neuropsychiatric manifestation in COVID-19 patients has been described in very few literatures. Our aim of the study was to find out the incidence of Psychosis in COVID-19 patients and its association with elevated levels of inflammatory markers such as IL-6, CRP etc, and with that of elevated coagulation parameter such as D-dimer values. Severity of Pneumonia (by HRCT thorax), neuropsychiatric presentation of Psychosis and the various interventions received by the COVID-19 patients with Psychosis were also studied. Out of 2752 COVID-19 cases new onset COVID Psychosis was seen only in 36 cases with an incidence of 1.308%. Out of these, 30 cases were aged > 60 years (83.3%) with male predominance (n=25)(69.44%) Psychotic manifestations such as delusion, hallucinations and mania were seen in 34 (94%), 32(88.8%) and 28 (77.7%) cases respectively. Copyright © 2022, Research Journal of Pharmaceutical, Biological and Chemical Sciences. All Rights Reserved.
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Due to the rapid spread of COVID-19 as a global pandemic, it has become increasingly critical to have fast, cheap, and reliable tools to assist physicians in diagnosing COVID19. Several automated systems using deep learning techniques have demonstrated promising results by analyzing Computed Tomography (CT-scan) or X-ray data to complement conventional diagnostic tools. In this paper, we aim to emphasize the role of point-of-care ultrasound imaging using deep learning as a tool to detect COVID-19 more prominently. Ultrasound imaging is non-invasive and widely available in medical facilities all over the world. This paper presents an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. It classifies lung ultrasound (LUS) images of patients into COVID-19 and Non-COVID-19 categories. The lack of COVID-19 data makes it challenging to train a traditional CNN from scratch, so we have adapted a transfer learning approach instead of training the base classifiers VGG16, ResNet-50, and GoogLeNet. We apply the gained knowledge in the target domain of small lung ultrasound frames, considering the ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework is evaluated on a publicly available dataset, achieving 96.7% accuracy. The proposed architecture outperforms the state-of-the-art method on the same dataset and proves to be a reliable COVID-19 detector. © 2022 IEEE.
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The fast proliferation of the coronavirus around the globe has put several countries' healthcare systems in danger of collapsing. As a result, locating and separating COVID-19-positive patients is a critical task. Deep Learning approaches were used in several computer-aided automated systems that utilized chest computed tomography (CT-scan) or X-ray images to create diagnostic tools. However, current Convolutional Neural Network (CNN) based approaches cannot capture the global context because of inherent image-specific inductive bias. These techniques also require large and labeled datasets to train the algorithm, but not many labeled COVID-19 datasets exist publicly. To mitigate the problem, we have developed a self-attention-based Vision Transformer (ViT) architecture using CT-scan. The proposed ViT model achieves an accuracy of 98.39% on the popular SARS-CoV-2 datasets, outperforming the existing state-of-the-art CNN-based models by 1%. We also provide the characteristics of CT scan images of the COVID-19-affected patients and an error analysis of the model's outcome. Our findings show that the proposed ViT-based model can be an alternative option for medical professionals for effective COVID-19 screening. The implementation details of the proposed model can be accessed at https://github.com/Pranabiitp/ViT. © 2022 IEEE.
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This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net © 2022 IEEE.
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Nowadays, the healthcare problem is one of the major crises in many parts of the world, especially the COVID-19 pandemic has exacerbated this to a greater extent. Many developing countries with inadequate healthcare systems are suffering greatly from this crisis to provide proper medical services. The reasons are the insufficient number of healthcare providers, costs of medical tests and equipment, lack of accessible points of care and data analysis, and lack of sufficient online healthcare facilities. However, research on the benefits of establishing e-health platforms to strengthen the conventional public-health system is limited—most of the research targets patients in specific disease groups. This paper focuses on an approach for designing a healthcare social media platform for services provisioning, consuming, enabling patients to find an alternate source of healthcare advice, and then building a collaborative health community for all kinds of people. Its usability and applicability have been experimented with as a prototype on Android-based smartphone devices. The results show six features and benefits that are distinct from existing approaches in the literature. In addition, the approach will be considered an affordable alternative to conventional healthcare in case of emergency treatment. © 2022 Slovene Society Informatika. All rights reserved.
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Introduction: Vaccines play an important role in the fight against diseases whose cure is unavailable. In the battle against pandemics such as Coronavirus Disease-2019 (COVID-19), the vaccine is the only available course of prevention. The hesitancy has been found all over the world, while some find it against their religious values, others are concerned about safety, or have doubts about its efficacy. Some are hesitant due to fear of needles while some show brass negligence. Being the second most populated country globally and a developing nation, India had faced its fair share of struggles with her citizens vaccinated. Even a minute percentage of people accounts for millions;hence, it is of utmost importance to get to the root of the causes of delay in vaccination.Aim: To find the causes of delay or hesitancy among the people attending COVID-19 vaccination centre of a tertiary care hospital of Kolkata, (a year after vaccines were introduced to the general population).Materials And Methods: A cross-sectional study was performed in the COVID-19 vaccination centre of Medical College Kolkata, West Bengal, India, from 14th January to 14th April 2022. Total 74 non medical (not related to healthcare work) people who had come for 1st or 2nd dose of COVID-19 vaccination were included in the study. A prestructured, pretested, prevalidated questionnaire was used to collect data from the participants of the study. The Likert scale comprising of nine questions were used to assess hesitancy. Data were analysed using Chi-square test. Binary logistic regression was done to confirm any predictability of occupation, literacy rate, age and gender on vaccine hesitancy.Results: The participants comprised of 45 (60.8%) females and 29 (39.2%) males, aged between 18 to 60 years with the mean age of 33.75 +/- 11.06 years. The participants included 22 (29.7%) people who had just taken their first dose. Twenty six (35.1%) participants were hesitant. The most common causes of hesitancy were individuals' fear of the vaccine and its impact on general health, unavailability of slots for vaccination and reluctance. Out of total, 58 (78.4%) people had faith in vaccines made in India and 48 (64.9%) persons believed that the vaccine would provide complete protection against COVID-19.Conclusion: People were mainly concerned about safety issues as adequate and reliable information was not available to them. Some of them ignored the importance of vaccination while some could not get vaccinated due to the unavailability of slots.
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Understanding first and second wave of covid19 Indian data along with its few selective states, we have realized a transition between two Sigmoid pattern with twice larger growth parameter and maximum values of cumulative data. As a result of those transition, time duration of second wave shrink to half of that first wave with four times larger peak values. Realizing first and second wave Sigmoid pattern due to covid19 virus and its mutated variant— δ virus respectively, third wave was mapped by another Sigmoid pattern with three times larger growth parameter than that of first wave. After understanding the crossing zone among first, second and third wave curves due to covid19, δ and omicron respectively, a hidden Sigmoid pattern due to mutated δ+ virus is identified in between δ and omicron. It is really interesting that entire covid19 data of India can be easily (offcourse grossly) understood by simple algebraic expressions of Sigmoid function and we can identify 4 Sigmoid patterns due to covid19 virus and its 3 dominant variants. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The worldwide breakout of the novel COVID-19 has resulted in one of the worst epidemics in modern times since World War II. Although various vaccinations are being produced, their efficacy remains a considerable hurdle. This is especially true when new virus strains emerge. The main challenge to combating this pandemic is diagnosing and isolating COVID-19 positive cases as early as possible. As a result, COVID-19 needs to be detected early and accurately to prevent its spread. This paper proposes a computer-aided automated COVID-19 detection tool based on Computed Tomography (CT-scan) images of lungs. The proposed approach applies an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. The lack of COVID-19 data makes it challenging to train a standard CNN from scratch, so we use a transfer learning approach instead of training the base classifiers, VGG-16, InceptionResnetV2, and Xception. We apply the gained knowledge in the target domain of small CT-scan data, considering ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework achieves 98.99% accuracy on a publicly available dataset and outperforms the existing state-of-the-art methods. Experimental results and comparative analysis with baselines establish the need and effectiveness of our proposed model. © 2022 IEEE.
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Introduction: Worldwide there was a pandemic of novel corona virus infection in which one of the major concern was the risk of thrombosis and the mortality associated with it. Aim(s): In this study our aim was to observe the changes in D-dimer levels during disease progression and its correlations with severity of Pneumonia, duration of hospital stay and mortality of COVID-19 patients. Material(s) and Method(s): In this study we reported the clinical, radiological and pathological laboratory results of 432 cases of confirmed COVID-19 infection. In these patients their clinical presentation, concentration of D-dimer, coagulation parameters, CBC, severity of Pneumonia on HRCT, hospital stay and higher mortality were retrospectively analyzed. Result(s): All the statistical variables were expressed in % and compared withx2 test. Out of the 432 cases in 45 cases (10.41%) the D-dimer values were >2.4microg/ml and in 15 cases the value were very high (3.47%). When correlated these patients found to have severe degree of pneumonia, longer hospital stay and higher mortality rate in comparison to patients with D-dimer level of <2.4 microg/ml. Conclusion(s): D-dimer level could be used as an early marker for the clinical classification, risk stratification and improved management of COVID-19 patients. Copyright © 2022, Institute of Medico-legal Publication. All rights reserved.
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In the early months of the COVID-19 pandemic in India, due to strict lockdown, the family members of the victims of COVID-19 had to witness the dying and death of their relatives in solitude, improper funerals, and the absence of death rituals. After in-depth interviews with twelve relatives of seven deceased patients conducted more than a year after experiencing those deaths of loved ones, it was found that most of them had been struggling with long-term complicated grief without a sense of resolution. As funerals and death rituals, following the work of Van Gennep in his ‘Rites of Passage’, ensure the transition of grievers from a preliminal state by preparing for the imminent loss to a postliminal renovated stable state by reabsorbing them into the collective social and cultural conditions, the absence of that compels the mourners to get stuck in a liminal state, or limbo.
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Background: Recent evidence suggests a bidirectional relationship between COVID-infection and new-onset diabetes (NOD) presenting with DKA. Methodology: This one-year prospective study comprised of 29 COVID-negative DKA (controls) and 52 COVID-positive-DKA patients (18 NOD, 15 T1DM ,T2DM) . NOD were previously normoglycemic and negative for GAD/IA-2/ZnT8 autoantibodies. After 75g- OGTT with estimation of glucose, C-peptide, FFA and insulin at 0,15, 30,45, 60,90 ,120, 150 and 180minutes, Insulin secretion rate (ISR) [C-peptide-deconvolution] , Hepatic insulin sensitivity [AUC-glucose × AUC-insulin during first 30-minutes of OGTT ], Peripheral insulin sensitivity [ dG/dt ÷ mean plasma insulin concentration;dG/dt rate of decline in plasma glucose concentration]were calculated alongwith Metabolomics and Adipose tissue gene expression. All tests were performed at admission and 4, 8, and 12-months of followup. Results: At baseline, ISR in NOD was significantly reduced than controls (p=0.001) but similar to T1DM (p=0.15) . Nearly 83% (n=17) of NOD with DKA had near-complete recovery of ISR on follow-up compared to T1DM (all p<0.01) ,with non-remitters (n=3) having significantly worse admission Hba1c and IL-6 (all p<0.01) . NOD had significantly increased hepatic and peripheral insulin resistance compared to T1DM (all p<0.05) ,but similar to T2DM (all p>0.05) . Their Metabolomics revealed increased inflammatory phosphatidylcholines, that correlated with peripheral glucose uptake (p<0.01) ,while RNA sequencing showed significantly enhanced WNT5A , TLR4 (Toll-like Receptor-4) and RETN (resistin) than T1DM and T2DM (both p=0.001) . Conclusion: Our study provides novel insights into COVID-associated NOD with DKA. Majority have near-complete recovery of insulin secretion while simultaneous multi-tissue insulin resistance and inflammatory adipose tissue profiles persist as drivers of hyperglycemia.
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Background: The coronavirus disease or COVID-19 pandemic is the major global health crisis of the present time. Various rheumatological manifestations have been reported during or after COVID-19 infection, but data are scarce. In this observational study, we have tried to analyze the clinical characteristics of COVID-19 associated arthralgia/arthritis. Methods: We have collected the clinical data of 14 patients over the past 6 months who have developed arthralgia or arthritis during or after symptomatic COVID-19 infection, proven by a positive reverse transcription-polymerase chain reaction test from nasopharyngeal swab. Results: The most common symptoms during COVID-19 infection in the 14 patients were fever and myalgia, being present in 92.8% and 64.3% patients, respectively. Arthralgia/arthritis occurred at a mean interval of 20 days (range: 0-60 days). Knee was the most commonly involved joint (78.6%), followed by the wrist and metacarpophalangeal joints (each in 57.1%). Enthesitis was documented in 21.4% patients. The mean duration of COVID-19 associated arthralgia or arthritis was 53.9 days (range: 7-210 days). In 85.7% patients, joint pains improved within 2 months;in only a small proportion of patients (14.3%), joint pains persisted after 6 months. Nonsteroidal anti-inflammatory drugs (NSAIDs) (given in 64.3% patients) and corticosteroids (in 50%) were the most commonly prescribed and effective treatment options. Conclusion: COVID-19 infections mostly caused reactive arthritis, though acute and chronic arthritis is also seen. In the majority of cases, arthritis started about 3 weeks after COVID-19 infection and subsided within 2 months. NSAIDs and corticosteroids are the most effective treatment options.
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Purpose Taking a retrospective view, the present study aims to investigate the resilience of shared facilities (accommodation) across India in the post-pandemic period. More specifically, it explores the issues and challenges in implementing sustainable practices in the long run taking a dual perspective of both consumers and service providers. Design/methodology/approach A mixed-method study was pursued in exploring the future resilience of the shared facilities in the post-pandemic period. A multi-method triangulation approach was adopted involving both data collection and data analysis. Primary data was collected through focus group sessions and analysed through a grounded theory study. Whereas, secondary data was extracted from Twitter and processed through textual data mining using the NVivo (12 Pro) software. Critical themes and sentiments were explored through the dual study and a corroboration process was followed thereon to support the findings. Findings The dual study extracted major themes pertaining to the present pandemic scenario wherein recovery strategies are at the top priority for all tourism service providers. Among the major themes tourists 2019 passiveness towards the environment existing misconceptions with shared facilities and situational perspective emerged as critical issues worrying service providers in the post-pandemic period. Furthermore the sentiment analysis indicated a positive start to the recovery measures wherein both tourist and tour operators are confident to embrace and restore the shared facilities/business respectively with additional care and responsibilities. Originality/value The novelty of the study lies in the identification of critical themes and sentiments concerning the future resilience of the shared economy businesses post-pandemic period in Indian tourism which can have a generalized effect across the world. Moreover, the study corroborated the findings of the dual study where similarities among the themes were observed.