A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges.

Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.

Management Challenge of Rhino-Orbito-Cerebral Mucormycosis in Covid 19 Era: A Prospective Observational Study.

Mucormycosis is an uncommon but a fatal fungal infection that usually affects patients with altered immunity. The Rhizopus Oryzae is most common type and responsible for nearly 60% of mucormycosis cases in humans and also accounts for 90% of the Rhino-orbital-cerebral (ROCM) form. Our aim and objective was to study, the site of mucormycosis in nose and paranasal sinuses, adjacent site like orbit palate or intracranial involvement, co-morbid condition and management to be done in confirm mucormycosis patients. The most common sinuses involved are the ethmoid, maxillary followed by the frontal and sphenoid sinus. Diabetes mellitus is often associated with mucormycosis of the paranasal sinuses, as is corona virus infection; uncontrolled diabetes further increases the risk. Intra-orbital involvement is common, but intracranial involvement is rare. Extensive steroid and broad-spectrum antibiotic use for Covid-19 management may cause or exacerbate fungal disease. All the patients required surgical intervention along with medical treatment.

A LAG-based framework to overcome the challenges of the sustainable vaccine supply chain: an integrated BWM–MARCOS approach

PurposeEach individual needs to be vaccinated to control the spread of the COVID-19 pandemic in the shortest possible time. However, the vaccine distribution with an already strained supply chain in low- and middle-income countries (LMICs) will not be effective enough to vaccinate all the population in stipulated time. The purpose of this paper is to show that there is a need to revolutionize the vaccine supply chain (VSC) by overcoming the challenges of sustainable vaccine distribution.Design/methodology/approachAn integrated lean, agile and green (LAG) framework is proposed to overcome the challenges of the sustainable vaccine supply chain (SVSC). A hybrid best worst method (BWM)–Measurement of Alternatives and Ranking According to COmpromise Solution (MARCOS) methodology is designed to analyze the challenges and solutions.FindingsThe analysis shows that vaccine wastage is the most critical challenge for SVSC, and the coordination among stakeholders is the most significant solution followed by effective management support.Social implicationsThe result of the analysis can help the health care organizations (HCOs) to manage the VSC. The effective vaccination in stipulated time will help control the further spread of the virus, which will result in the normalcy of business and availability of livelihood for millions of people.Originality/valueTo the best of the author's knowledge, this is the first study to explore sustainability in VSC by considering the environmental and social impact of vaccination. The LAG-based framework is also a new approach in VSC to find the solution for existing challenges.

COVID-19: Consequences on pregnant women and neonates.

Introduction: Human species is confronting with a gigantic global COVID-19 pandemic. Initially, it was observed in Wuhan, China, and the COVID-19 cases spread across the globe with lightning speed and resulted in the 21st century pandemic. If scientific reports are taken care of, it is noteworthy that this virus possesses more specific characteristics due to its structure. The distinctive structure has a higher binding affinity with angiotensin-converting enzyme 2 (ACE2) protein, and this is used as an access point to gain access to hosts. Methods: A complete literature search was conducted using PubMed, Google Scholar, SciFinder, and deep-diving Google Search using keywords such as "Pregnancy, COVID-19, Newborn, Fetus, Coronavirus 2019, Neonate, Pregnant women, and vertical transmission". Result and discussion: The SARS-CoV-2 virus is unlike its former analogs: SARS-CoV, and MERS-CoV in 2002 and 2012, respectively, or anything mankind has faced earlier concerning viciousness, global spread, and gravity of a causative agent. The current review has delved into articles published in various journals worldwide including the latest studies on the impact of COVID-19 on pregnant women and neonates and has discussed complications and challenges, psychological health, immunological response, vertical transmission, concurrent disorders, vaccine debate, management recommendations, recent news of the approval of COVID-19 vaccine for 6 months and older babies, and future perspectives.

Quality and selling price dependent sustainable perishable inventory policy: Lessons from Covid-19 pandemic

This paper addresses the impact of the Covid-19 lockdown on the warehousing of perishable items facing demand-side shocks, mainly those with selling price and product quality dependent demand, for example, fresh fruits, meats, vegetables, packed foods, etc. Along with demand-side issues, such an inventory system consumes a significant amount of energy in terms of freshness, increasing carbon tax and dwindling the firm's total profit. We formulate two-warehouse inventory models of perishables items using the first-in-first-out (FIFO) dispatching policy under two different Covid-19 lockdown scenarios. The two-warehouse system primarily consists of an owned warehouse (OW) and a rented warehouse (RW). Two different lockdown scenarios are considered as;(i) the lockdown during the consumption of goods in OW and (ii) the lockdown during the consumption of goods in RW. The demand rate is assumed to decline and surge by a finite volume as lockdown is forced and relaxed. The proposed models help in assessing the impact of lockdown on (i) product quality, (ii) product cost, (iii) inventory level, (iv) freshness keeping efforts, (v) investment in green technologies, and (vi) carbon cap and trade policy. We determine the above six parameters to maximize the firm's total profit. The key findings of this model suggest that yield is primarily affected due to carbon cap and trade policy, lockdown period, item price, backlogging, and variation in the holding costs in OW and RW. These models may assist the small, medium, and large firms involved in perishable or cold supply chains to assess the effect of Covid-19 like disruption and take corrective measures to maximize their profit.

A fuzzy multi-criteria framework to identify barriers and enablers of the next-generation vaccine supply chain

PurposeDue to the introduction of new vaccines in the child immunization program and inefficient vaccine supply chain (VSC), the universal immunization program (UIP), India is struggling to provide a full schedule of vaccination to the targeted children. In this paper, the authors investigate the critical factors for improving the performance of the existing VSC system by implementing the next-generation vaccine supply chain (NGVSC) in India.Design/methodology/approachThe authors design a fuzzy multi-criteria framework using a fuzzy analytical hierarchical process (FAHP) and fuzzy multi-objective optimization on the basis of ratio analysis (FMOORA) to identify and analyze the critical barriers and enablers for the implementation of NGVSC. Further, the authors carry out a numerical simulation to validate the model.FindingsThe outcome of the analysis contends that demand forecasting is the topmost supply chain barrier and sustainable financing is the most important/critical enabler to facilitate the implementation of the NGVSC. In addition, the simulation reveals that the results of the study are reliable.Social implicationsThe findings of the study can be useful for the child immunization policymakers of India and other developing countries to design appropriate strategies for improving existing VSC performance by implementing the NGVSC.Originality/valueTo the best of the authors' knowledge, the study is the first empirical study to propose the improvement of VSC performance by designing the NGVSC.

Evaluation of immune evasion in SARS-CoV-2 Delta and Omicron variants.

Emerging SARS-CoV-2 variants with higher transmissibility and immune escape remain a persistent threat across the globe. This is evident from the recent outbreaks of the Delta (B.1.617.2) and Omicron variants. These variants have originated from different continents and spread across the globe. In this study, we explored the genomic and structural basis of these variants for their lineage defining mutations of the spike protein through computational analysis, protein modeling, and molecular dynamic (MD) simulations. We further experimentally validated the importance of these deletion mutants for their immune escape using a pseudovirus-based neutralization assay, and an antibody (4A8) that binds directly to the spike protein's NTD. Delta variant with the deletion and mutations in the NTD revealed a better rigidity and reduced flexibility as compared to the wild-type spike protein (Wuhan isolate). Furthermore, computational studies of 4A8 monoclonal antibody (mAb) revealed a reduced binding of Delta variant compared to the wild-type strain. Similarly, the MD simulation data and virus neutralization assays revealed that the Omicron also exhibits immune escape, as antigenic beta-sheets appear to be disrupted. The results of the present study demonstrate the higher possibility of immune escape and thereby achieved better fitness advantages by the Delta and Omicron variants, which warrants further demonstrations through experimental evidences. Our study, based on in-silico computational modelling, simulations, and pseudovirus-based neutralization assay, highlighted and identified the probable mechanism through which the Delta and Omicron variants are more pathogenically evolved with higher transmissibility as compared to the wild-type strain.

Voice Features of Sustained Phoneme as COVID-19 Biomarker.

BACKGROUND: The COVID-19 pandemic has resulted in enormous costs to our society. Besides finding medicines to treat those infected by the virus, it is important to find effective and efficient strategies to prevent the spreading of the disease. One key factor to prevent transmission is to identify COVID-19 biomarkers that can be used to develop an efficient, accurate, noninvasive, and self-administered screening procedure. Several COVID-19 variants cause significant respiratory symptoms, and thus a voice signal may be a potential biomarker for COVID-19 infection. AIM: This study investigated the effectiveness of different phonemes and a range of voice features in differentiating people infected by COVID-19 with respiratory tract symptoms. METHOD: This cross-sectional, longitudinal study recorded six phonemes (i.e., /a/, /e/, /i/, /o/, /u/, and /m/) from 40 COVID-19 patients and 48 healthy subjects for 22 days. The signal features were obtained for the recordings, which were statistically analyzed and classified using Support Vector Machine (SVM). RESULTS: The statistical analysis and SVM classification show that the voice features related to the vocal tract filtering (e.g., MFCC, VTL, and formants) and the stability of the respiratory muscles and lung volume (Intensity-SD) were the most sensitive to voice change due to COVID-19. The result also shows that the features extracted from the vowel /i/ during the first 3 days after admittance to the hospital were the most effective. The SVM classification accuracy with 18 ranked features extracted from /i/ was 93.5% (with F1 score of 94.3%). CONCLUSION: A measurable difference exists between the voices of people with COVID-19 and healthy people, and the phoneme /i/ shows the most pronounced difference. This supports the potential for using computerized voice analysis to detect the disease and consider it a biomarker.

Fine Tuning CNN for COVID-19 Patterns Detection From Chest Radiographs

The COVID-19 pandemic has crumbled health systems all over the world. Quick and accurate detection of coronavirus infection plays an important role in timely referral of physicians and control transmission of the disease. RT-PCR is the most widely test used for identification of COVID-19 patients, but it takes long to deliver the report. Researchers around the world are looking for alternative machine learning techniques including deep learning to assist the medical experts for early COVID-19 disease diagnosis from medical imaging such as chest films. This study proposes an enhanced convolutional neural network (EConvNet) model for the presence and absence of coronavirus disease from chest radiographs to contain this pandemic. The model is accurate compared to the traditional machine learning algorithms (RF, SVM, etc.). The suggested CNN model is approximately as accurate as the classifiers based on transfer learning (such as InceptionV3, VGG16, and Densenet121). Despite being simple in terms of number of parameters learnt, it takes less training time and demands less memory.

Challenges in Accessing and Delivering Maternal and Child Health Services during the COVID-19 Pandemic: A Cross-Sectional Rapid Survey from Six States of India.

BACKGROUND/OBJECTIVES: Globally, the COVID-19 pandemic and its prevention and control policies have impacted maternal and child health (MCH) services. This study documents the challenges faced by patients in accessing MCH services, and the experiences of health care providers in delivering those services during the COVID-19 outbreak, explicitly focusing on the lockdown period in India. METHODS: A cross-sectional study (rapid survey) was conducted in 18 districts from 6 states of India during March to June, 2020. The sample size included 540 MCH patients, 18 gynaecologists, 18 paediatricians, 18 district immunisation officers and 108 frontline health workers. Bivariate analysis and multivariable analysis were used to assess the association between sociodemographic characteristics, and challenges faced by the patients. RESULTS: More than one-third of patients (n = 212; 39%) reported that accessing MCH services was a challenge during the lockdown period, with major challenges being transportation-related difficulties (n = 99; 46%) unavailability of hospital-based services (n = 54; 23%) and interrupted outreach health services (n = 39; 18.4%). The supply-side challenges mainly included lack of infrastructural preparedness for outbreak situations, and a shortage of human resources. CONCLUSIONS/RECOMMENDATIONS: A holistic approach is required that focuses on both preparedness and response to the outbreak, as well reassignment and reinforcement of health care professionals to continue catering to and maintaining essential MCH services during the pandemic.

Scoping review of published research on medical education in India during the Covid-19 pandemic.

Background Medical educators in India made rapid adjustments to maintain continuity and integrity of medical education in the midst of disruption caused by the Covid-19 pandemic. However, there are concerns regarding achievement of competence by undergraduate medical students due to inadequate clinical exposure. We explored the focus of initiatives from medical educators in India by a scoping review of published articles on developments in medical education during the pandemic to map concepts, main sources and the literature available in PubMed. Methods We did this scoping review of published articles in PubMed database in four steps: (i) identification of research questions; (ii) identification of relevant studies; (iii) selection of studies meeting inclusion and exclusion criteria, and charting of data; and (iv) collating the summary and reporting of results. Manual content analysis was done to derive frequencies of variables. Results Of the 52 articles identified, 22 met the requirements. Most studies (68.2%) were published in 2020. Half of the studies were conducted among undergraduate students and the remaining among postgraduates (27.3%), faculty (18.2%) and interns (4.5%). All the studies were evaluations at Kirkpatrick level-1 (18; 81.8%) and level-2 (4; 18.2%). Most of the studies (9, 41%) focused on exploration of perspectives about online learning among students and faculty, 9 (27.3%) on teaching- learning, 4 (18.2%) on formative assessment and 3 (13.6%) on summative assessment. Conclusions Most studies were evaluations at Kirkpatrick level-1 and level-2 among undergraduate medical students with a focus on conceptual understanding.

Seroprevalence of anti SARS-CoV-2 IgG antibodies among adults in Jammu district, India: A community-based study.

Background & objectives: Serology testing is essential for immunological surveillance in the population. This serosurvey was conducted to ascertain the cumulative population immunity against SARS-CoV-2 among adults in Jammu district and to understand the association of seropositivity with sociodemographic and clinical correlates. Methods: On September 30 and October 1, 2020, a household survey was done in 20 villages/wards chosen from 10 health blocks in district Jammu, India. Demographic, clinical and exposure information was collected from 2000 adults. Serum samples were screened for IgG antibodies using COVID Kavach MERILISA kit. Tests of association were used to identify risk factors associated with IgG positivity. Crude odds ratio with 95 per cent confidence intervals (CIs) was calculated during univariate analysis followed by logistic regression. Results: Overall adjusted seroprevalence for SARS-CoV-2 was 8.8 per cent (95% CI: 8.78-8.82); it varied from 4.1 per cent in Chauki choura to 16.7 per cent Pallanwalla across 10 blocks in the district. Seropositivity was observed to be comparatively higher in 41-50 and 61-70 yr age groups, among males and in rural areas. Fever, sore throat, cough, dyspnoea, myalgias, anosmia, ageusia, fatigue, seizures, history of exposure, medical consultation, hospitalization and missing work showed significant association with seropositivity on univariate analysis. On logistic regression, only sore throat, myalgia and missing work showed significant adjusted odds of IgG positivity. Extrapolation to adult population suggested that exposure to SARS-CoV-2 was 14.4 times higher than reported cases, translating into Infection fatality rate of 0.08 per cent. Interpretation & conclusions: Since a major part of population was immunologically naive, all efforts to contain COVID-19 need to be vigorously followed while these baseline results provide an important yardstick to monitor the trends of COVID-19 and guide locally appropriate control strategies in the region.

Evaluation of Performance of Detection of Immunoglobulin G and Immunoglobulin M Antibody Against Spike Protein of SARS-CoV-2 by a Rapid Kit in a Real-Life Hospital Setting.

Background: Antibody testing is often used for serosurveillance of coronavirus disease 2019 (COVID-19). Enzyme-linked immunosorbent assay and chemiluminescence-based antibody tests are quite sensitive and specific for such serological testing. Rapid antibody tests against different antigens are developed and effectively used for this purpose. However, their diagnostic efficiency, especially in real-life hospital setting, needs to be evaluated. Thus, the present study was conducted in a dedicated COVID-19 hospital in New Delhi, India, to evaluate the diagnostic efficacy of a rapid antibody kit against the receptor-binding domain (RBD) of the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: Sixty COVID-19 confirmed cases by reverse transcriptase-polymerase chain reaction (RT-PCR) were recruited and categorized as early, intermediate, and late cases based on the days passed after their first RT-PCR-positive test report, with 20 subjects in each category. Twenty samples from pre-COVID era and 20 RT-PCR-negative collected during the study period were taken as controls. immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies against the RBD of the spike (S) protein of SARS-CoV-2 virus were detected by rapid antibody test and compared with the total antibody against the nucleocapsid (N) antigen of SARS-CoV-2 by electrochemiluminescence-based immunoassay (ECLIA). Results: The detection of IgM against the RBD of the spike protein by rapid kit was less sensitive and less specific for the diagnosis of SARS-CoV-2 infection. However, diagnostic efficacy of IgG by rapid kit was highly sensitive and specific when compared with the total antibody against N antigen measured by ECLIA. Conclusion: It can be concluded that detection of IgM against the RBD of S protein by rapid kit is less effective, but IgG detection can be used as an effective diagnostic tool for SARS-CoV-2 infection in real-life hospital setting.

Blockchain technology and vaccine supply chain: Exploration and analysis of the adoption barriers in the Indian context

The vaccine distribution system, being a bio-pharmaceutical cold chain, is a complicated and sensitive system that must be effectively managed and maintained due to its direct impact on public health. However, vaccine supply chains continue to be affected by concerns, including vaccine expiry, inclusion of counterfeit vaccines, and vaccine record fraud. The blockchain technology integrated with the Internet of Things (IoT) can create a solution for global vaccine distributions with improved trust, transparency, traceability, and data management, which will help monitor the cold chain, tackle counterfeit drugs, surveillance, and waste management. Several theoretical models for vaccine management with blockchain have recently been published, and a few pilot studies for COVID-19 vaccine management using blockchain have been started in India. Still, full-scale adoption of blockchain technology in vaccine distribution and management has yet to be achieved due to underlying barriers. This study explores the adoption barriers utilizing Technology-Organization-Environment (TOE) framework with the help of extant literature and inputs from administrators, academics, immunization, and blockchain experts and then analyzed using the Delphi and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. The finding shows that the requirement of change in organization structure and policies is the most prominent barrier, and the barrier related to requirement of large-scale IoT infrastructure and lack of technical expertise are the most impactful barriers. The theoretical contribution of this study lies in the identification and analysis of barriers that should be addressed to achieve blockchain technology adoption in the vaccine supply chain.

Surveillance and Molecular Characterization of SARS-CoV-2 Infection in Non-Human Hosts in Gujarat, India.

Since December 2019, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has been spreading worldwide, triggering one of the most challenging pandemics in the human population. In light of the reporting of this virus in domestic and wild animals from several parts of the world, a systematic surveillance study was conceptualized to detect SARS-CoV-2 among species of veterinary importance. Nasal and/or rectal samples of 413 animals (dogs n= 195, cattle n = 64, horses n = 42, goats n = 41, buffaloes n = 39, sheep n = 19, cats n = 6, camels n = 6, and a monkey n = 1) were collected from different places in the Gujarat state of India. RNA was extracted from the samples and subjected to RT-qPCR-based quantification of the target sequences in viral nucleoprotein (N), spike (S), and ORF1ab genes. A total of 95 (23.79%) animals were found positive, comprised of n = 67 (34.35%) dogs, n= 15 (23.43%) cattle, and n = 13 (33.33%) buffaloes. Whole SARS-CoV-2 genome sequencing was done from one sample (ID-A4N, from a dog), where 32 mutations, including 29 single-nucleotide variations (SNV) and 2 deletions, were detected. Among them, nine mutations were located in the receptor binding domain of the spike (S) protein. The consequent changes in the amino acid sequence revealed T19R, G142D, E156-, F157-, A222V, L452R, T478K, D614G, and P681R mutations in the S protein and D63G, R203M, and D377Y in the N protein. The lineage assigned to this SARS-CoV-2 sequence is B.1.617.2. Thus, the present study highlights the transmission of SARS-CoV-2 infection from human to animals and suggests being watchful for zoonosis.

A hybrid framework to model resilience in the generic medicine supply chain of MSMEs

Purpose One of the most important components of healthcare is the timely delivery of pharmaceutical products, such as life-saving medicines. However, disruptions like COVID-19 bring new challenges and risks to the pharmaceutical supply chain (PSC) and healthcare organizations that impact their operational performance. This study focuses on mitigating risks in India's generic medicine supply chain (GMSC) as a result of various disruptions, which can assist policymakers develop appropriate plans and strategies to build resilience in the Jan Aushadhi Scheme (JAS) of micro, small and medium enterprises (MSMEs) in order to improve their overall performance. Design/methodology/approach Risk-causing vulnerabilities and resilience capabilities are identified from the literature review and expert's opinions. Following that, the vulnerabilities are classified into cause-and-effect vulnerabilities, and supply chain resilient capabilities (SCRCs) are measured using a hybrid fuzzy DEMATEL and best worst method (FDEMATEL-BMW) framework. Findings The outcome of the study reveals that transportation breakdown, loss of human resources and loss of suppliers are the potential risk-causing vulnerabilities that lead to vulnerabilities like shortages of medicines, loss of in-hand stock qualities and loss of sales/revenue. In addition, the analysis suggests that the sustainability of an organization with maximum weightage is the critical factor for building resilience in GMSC followed by flexibility, agility and visibility. Practical implications The integration of resilience into Jan Aushadhi GMSC can help in managing disruptions efficiently and effectively to mitigate risk and optimize MSMEs overall performance. Originality/value To the best of the authors' knowledge, this work will be the first of its kind to model resilience in GMSC of MSMEs using a hybrid framework.

Nasopharyngeal microbiome of COVID-19 patients revealed a distinct bacterial profile in deceased and recovered individuals.

The bacterial co-infections in SARS-CoV-2 patients remained the least explored subject of clinical manifestations that may also determine the disease severity. Nasopharyngeal microbial community structure within SARS-CoV-2 infected patients could reveal interesting microbiome dynamics that may influence the disease outcomes. Here, in this research study, we analyzed distinct nasopharyngeal microbiome profile in the deceased (n = 48) and recovered (n = 29) COVID-19 patients and compared it with control SARS-CoV-2 negative individuals (control) (n = 33). The nasal microbiome composition of the three groups varies significantly (PERMANOVA, p-value <0.001), where deceased patients showed higher species richness compared to the recovered and control groups. Pathogenic genera, including Corynebacterium (LDA score 5.51), Staphylococcus, Serratia, Klebsiella and their corresponding species were determined as biomarkers (p-value <0.05, LDA cutoff 4.0) in the deceased COVID-19 patients. Ochrobactrum (LDA score 5.79), and Burkholderia (LDA 5.29), were found in the recovered group which harbors ordinal bacteria (p-value <0.05, LDA-4.0) as biomarkers. Similarly, Pseudomonas (LDA score 6.19), and several healthy nasal cavity commensals including Veillonella, and Porphyromonas, were biomarkers for the control individuals. Healthy commensal bacteria may trigger the immune response and alter the viral infection susceptibility and thus, may play important role and possible recovery that needs to be further explored. This research finding provide vital information and have significant implications for understanding the microbial diversity of COVID-19 patients. However, additional studies are needed to address the microbiome-based therapeutics and diagnostics interventions.

Genetic sequencing detected the SARS-CoV-2 delta variant in wastewater a month prior to the first COVID-19 case in Ahmedabad (India).

Wastewater-based genomic surveillance can identify a huge majority of variants shed by the infected individuals within a population, which goes beyond genomic surveillance based on clinical samples (i.e., symptomatic patients only). We analyzed four samples to detect key mutations in the SARS-CoV-2 genome and track circulating variants in Ahmedabad during the first wave (Sep/Nov 2020) and before the second wave (in Feb 2021) of COVID-19 in India. The analysis identified a total of 34 mutations in the spike protein across samples categorized into 23 types. The spike protein mutations were linked to the VOC-21APR-02; B.1.617.2 lineage (Delta variant) with 57% frequency in wastewater samples of Feb 2021. The key spike protein mutations were T19R, L452R, T478K, D614G, & P681R and deletions at 22029 (6 bp), 28248 (6 bp), & 28271 (1 bp). Interestingly, these mutations were not seen in the samples from Sep/Nov 2020 but did appear before the massive second wave of COVID-19 cases, which in India started in early April 2021. In fact, genetic traces of the Delta variant were found in samples of early Feb 2021, more than a month before the first clinically confirmed case of this in March 2021 in Ahmedabad, Gujarat. The present work describes the circulating of SARS-CoV-2 variants in Ahmedabad and confirms the consequential value of wastewater surveillance for the early detection of variants of concerns (VOCs). Such monitoring must be included as a major component of future health protection systems.

Quality and selling price dependent sustainable perishable inventory policy: Lessons from Covid-19 pandemic

This paper addresses the impact of the Covid-19 lockdown on the warehousing of perishable items facing demand-side shocks, mainly those with selling price and product quality dependent demand, for example, fresh fruits, meats, vegetables, packed foods, etc. Along with demand-side issues, such an inventory system consumes a significant amount of energy in terms of freshness, increasing carbon tax and dwindling the firm's total profit. We formulate two-warehouse inventory models of perishables items using the first-in-first-out (FIFO) dispatching policy under two different Covid-19 lockdown scenarios. The two-warehouse system primarily consists of an owned warehouse (OW) and a rented warehouse (RW). Two different lockdown scenarios are considered as;(i) the lockdown during the consumption of goods in OW and (ii) the lockdown during the consumption of goods in RW. The demand rate is assumed to decline and surge by a finite volume as lockdown is forced and relaxed. The proposed models help in assessing the impact of lockdown on (i) product quality, (ii) product cost, (iii) inventory level, (iv) freshness keeping efforts, (v) investment in green technologies, and (vi) carbon cap and trade policy. We determine the above six parameters to maximize the firm's total profit. The key findings of this model suggest that yield is primarily affected due to carbon cap and trade policy, lockdown period, item price, backlogging, and variation in the holding costs in OW and RW. These models may assist the small, medium, and large firms involved in perishable or cold supply chains to assess the effect of Covid-19 like disruption and take corrective measures to maximize their profit.

Evaluation of Performance of Detection of Immunoglobulin G and Immunoglobulin M Antibody Against Spike Protein of SARS-CoV-2 by a Rapid Kit in a Real-Life Hospital Setting

Background Antibody testing is often used for serosurveillance of coronavirus disease 2019 (COVID-19). Enzyme-linked immunosorbent assay and chemiluminescence-based antibody tests are quite sensitive and specific for such serological testing. Rapid antibody tests against different antigens are developed and effectively used for this purpose. However, their diagnostic efficiency, especially in real-life hospital setting, needs to be evaluated. Thus, the present study was conducted in a dedicated COVID-19 hospital in New Delhi, India, to evaluate the diagnostic efficacy of a rapid antibody kit against the receptor-binding domain (RBD) of the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods Sixty COVID-19 confirmed cases by reverse transcriptase–polymerase chain reaction (RT-PCR) were recruited and categorized as early, intermediate, and late cases based on the days passed after their first RT-PCR–positive test report, with 20 subjects in each category. Twenty samples from pre-COVID era and 20 RT-PCR–negative collected during the study period were taken as controls. immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies against the RBD of the spike (S) protein of SARS-CoV-2 virus were detected by rapid antibody test and compared with the total antibody against the nucleocapsid (N) antigen of SARS-CoV-2 by electrochemiluminescence-based immunoassay (ECLIA). Results The detection of IgM against the RBD of the spike protein by rapid kit was less sensitive and less specific for the diagnosis of SARS-CoV-2 infection. However, diagnostic efficacy of IgG by rapid kit was highly sensitive and specific when compared with the total antibody against N antigen measured by ECLIA. Conclusion It can be concluded that detection of IgM against the RBD of S protein by rapid kit is less effective, but IgG detection can be used as an effective diagnostic tool for SARS-CoV-2 infection in real-life hospital setting.