The supply is there. So why can't pregnant and breastfeeding women in rural India get the COVID-19 vaccine?

Despite COVID-19 vaccines being available to pregnant women in India since summer 2021, little is known about vaccine uptake among this high need population. We conducted mixed methods research with pregnant and recently delivered rural women in northern India, consisting of 300 phone surveys and 15 in-depth interviews, in November 2021. Only about a third of respondents were vaccinated, however, about half of unvaccinated respondents reported that they would get vaccinated now if they could. Fears of harm to the unborn baby or young infant were common (22% of unvaccinated women). However, among unvaccinated women who wanted to get vaccinated, the most common barrier reported was that their health care provider refused to provide them the vaccine. Gender barriers and social norms also played a role, with family members restricting women's access. Trust in the health system was high, however, women were most often getting information about COVID-19 vaccines from sources that they did not trust, and they knew they were getting potentially poor-quality information. Qualitative data shed light on the barriers women faced from their family and health care providers but described how as more people got the vaccine that norms were changing. These findings highlight how pregnant women in India have lower vaccination rates than the general population, and while vaccine hesitancy does play a role, structural barriers from the health care system also limit access to vaccines. Interventions must be developed that target household decision-makers and health providers at the community level, and that take advantage of the trust that rural women already have in their health care providers and the government. It is essential to think beyond vaccine hesitancy and think at the system level when addressing this missed opportunity to vaccinate high risk pregnant women in this setting.

Mutational characterization of Omicron SARS-CoV-2 lineages circulating in Chhattisgarh, a central state of India.

Introduction: The emergence of the Omicron SARS-CoV-2 variant from various states of India in early 2022 has caused fear of its rapid spread. The lack of such reports from Chhattisgarh (CG), a central state in India, has prompted us to identify the Omicron circulating lineages and their mutational dynamics. Materials and methods: Whole-genome sequencing (WGS) of SARS-CoV-2 was performed in 108 SARS-CoV-2 positive combined samples of nasopharyngeal and oropharyngeal swabs obtained from an equal number of patients. Results: All 108 SARS-CoV-2 sequences belonged to Omicron of clade 21L (84%), 22B (11%), and 22D (5%). BA.2 and its sub-lineages were predominantly found in 93.5% of patients, BA.5.2 and its sub-lineage BA.5.2.1 in 4.6% of patients, and B.1.1.529 in 2% of patients. Various BA.2 sub-lineages identified were BA.2 (38%), BA.2.38 (32%), BA.2.75 (9.25%), BA.2.56, BA.2.76, and BA.5.2.1 (5% each), BA.2.74 (4.6%), BA.5.2.1 (3.7%), BA.2.43 and B.1.1.529 (1.8% each), and BA.5.2 (0.9%). Maximum mutations were noticed in the spike (46), followed by the nucleocapsid (5), membrane (3), and envelope (2) genes. Mutations detected in the spike gene of different Omicron variants were BA.1.1.529 (32), BA.2 (44), BA.2.38 (37), BA.2.43 (38), BA.2.56 (30), BA.2.74 (31), BA.2.75 (37), BA.2.76 (32), BA.5.2, and BA.5.2.1 (38 similar mutations). The spike gene showed the signature mutations of T19I and V213G in the N-terminal domain (NTD), S373P, S375F, T376A, and D405N in receptor-binding domain (RBD), D614G, H655Y, N679K, and P681H at the furin cleavage site, N764K and D796K in fusion peptide, and Q954H and N969K in heptapeptide repeat sequence (HR)1. Notably, BA.2.43 exhibited a novel mutation of E1202Q in the C terminal. Other sites included ORF1a harboring 13 mutations followed by ORF1b (6), ORF3a (2), and ORF6 and ORF8 (1 mutation each). Conclusion: BA.2 followed by BA.2.38 was the predominant Omicron lineage circulating in Chhattisgarh. BA.2.75 could supersede other Omicron due to its mutational consortium advantage. The periodical genomic monitoring of Omicron variants is thus required for real-time assessment of circulating strains and their mutational-induced severity.

A novel PRFB decomposition for non-stationary time-series and image analysis

This work presents a novel perfect reconstruction filterbank decomposition (PRFBD) method for nonlinear and non-stationary time-series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach based on Fourier representation (FR), is shown to be a special case of the proposed PRFBD. In addition, adaptive Fourier–Gauss decomposition (FGD) based on FR and Gaussian filters, and adaptive Fourier–Butterworth decomposition (FBD) based on Butterworth filters are developed as the other special cases of the proposed PRFBD method. The proposed theory of PRFBD can decompose any signal (time-series, image, or other data) into a set of desired number of Fourier intrinsic band functions (FIBFs) that follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters, is also presented. We performed an extensive analysis on both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches.

Mosaic Recombination Inflicted Various SARS-CoV-2 Lineages to Emerge into Novel Virus Variants: a Review Update.

Human Coronaviruses (hCoVs) belongs to the enormous and dissimilar family of positive-sense, non-segmented, single-stranded RNA viruses. The RNA viruses are prone to high rates of mutational recombination resulting in emergence of evolutionary variant to alter various features including transmissibility and severity. The evolutionary changes affect the immune escape and reduce effectiveness of diagnostic and therapeutic measures by becoming undetectable by the currently available diagnostics and refractory to therapeutics and vaccines. Whole genome sequencing studies from various countries have adequately reported mosaic recombination between different lineage strain of SARS-CoV-2 whereby RNA dependent RNA polymerase (RdRp) gene reconnects with a homologous RNA strand at diverse position. This all lead to evolutionary emergence of new variant/ lineage as evident with the emergence of XBB in India at the time of writing this review. The continuous periodical genomic surveillance is utmost required for understanding the various lineages involved in recombination to emerge into hybrid variant. This may further help in assessing virus transmission dynamics, virulence and severity factor to help health authorities take appropriate timely action for prevention and control of any future COVID-19 outbreak.

The supply is there. So why can't pregnant and breastfeeding women in rural India get the COVID-19 vaccine?

Despite COVID-19 vaccines being available to pregnant women in India since summer 2021, little is known about vaccine uptake among this high need population. We conducted mixed methods research with pregnant and recently delivered rural women in northern India, consisting of 300 phone surveys and 15 in-depth interviews, in November 2021. Only about a third of respondents were vaccinated, however, about half of unvaccinated respondents reported that they would get vaccinated now if they could. Fears of harm to the unborn baby or young infant were common (22% of unvaccinated women). However, among unvaccinated women who wanted to get vaccinated, the most common barrier reported was that their health care provider refused to provide them the vaccine. Gender barriers and social norms also played a role, with family members restricting women's access. Trust in the health system was high, however, women were most often getting information about COVID-19 vaccines from sources that they did not trust, and they knew they were getting potentially poor-quality information. Qualitative data shed light on the barriers women faced from their family and health care providers but described how as more people got the vaccine that norms were changing. These findings highlight how pregnant women in India have lower vaccination rates than the general population, and while vaccine hesitancy does play a role, structural barriers from the health care system also limit access to vaccines. Interventions must be developed that target household decision-makers and health providers at the community level, and that take advantage of the trust that rural women already have in their health care providers and the government. It is essential to think beyond vaccine hesitancy and think at the system level when addressing this missed opportunity to vaccinate high risk pregnant women in this setting.

Identification of a promising inhibitor from Illicium verum (star anise) against the main protease of SARS-CoV-2: insights from the computational study.

SARS-CoV-2, the causing agent of coronavirus disease (COVID-19), first broke out in Wuhan and rapidly spread worldwide, resulting in a global health emergency. The lack of specific drugs against the coronavirus has made its spread challenging to control. The main protease (Mpro) is a key enzyme of SARS-CoV-2 used as a key target in drug discovery against the coronavirus. Medicines derived from plant phytoconstituents have been widely exploited to treat various diseases. The present study has evaluated the potential of Illicium verum (star anise) phytoconstituents against Mpro by implementing a computational approach. We performed molecular docking and molecular dynamics simulation study with a set of 60 compounds to identify their potential to inhibit the main protease (Mpro) of SARS-CoV-2. DFT study and post dynamics free energy calculations were also performed to strengthen the findings. The identified four compounds by docking study exhibited the highest potential compared to other selected phytoconstituents. Further, density functional theory (DFT) calculation, molecular dynamics simulation and post dynamics MM-GBSA energy calculation predicted Verimol-G as a potential compound, which formed stable interactions through the catalytic dyad residues. The HOMO orbital energy (-0.250038) from DFT and the post dynamics binding free energy calculation (-73.33 Kcal/mol) correlate, suggesting Verimol-G is the best inhibitor compared to the other phytoconstituents. This compound also complies with the ADME properties of drug likeliness. Thus, based on a computational study, we suggest that Verimol G may be developed as a potential inhibitor against the main protease to combat COVID-19.Communicated by Ramaswamy H. Sarma.

Using a national level cross-sectional study to develop a Hospital Preparedness Index (HOSPI) for Covid-19 management: A case study from India.

BACKGROUND: We developed a composite index-hospital preparedness index (HOSPI)-to gauge preparedness of hospitals in India to deal with COVID-19 pandemic. METHODS: We developed and validated a comprehensive survey questionnaire containing 63 questions, out of which 16 critical items were identified and classified under 5 domains: staff preparedness, effects of COVID-19, protective gears, infrastructure, and future planning. Hospitals empaneled under Ayushman Bharat Yojana (ABY) were invited to the survey. The responses were analyzed using weighted negative log likelihood scores for the options. The preparedness of hospitals was ranked after averaging the scores state-wise and district-wise in select states. HOSPI scores for states were classified using K-means clustering. FINDINGS: Out of 20,202 hospitals empaneled in ABY included in the study, a total of 954 hospitals responded to the questionnaire by July 2020. Domains 1, 2, and 4 contributed the most to the index. The overall preparedness was identified as the best in Goa, and 12 states/ UTs had scores above the national average score. Among the states which experienced high COVID-19 cases during the first pandemic wave, we identified a cluster of states with high HOSPI scores indicating better preparedness (Maharashtra, Tamil Nadu, Karnataka, Uttar Pradesh and Andhra Pradesh), and a cluster with low HOSPI scores indicating poor preparedness (Chhattisgarh, Delhi, Uttarakhand). INTERPRETATION: Using this index, it is possible to identify areas for targeted improvement of hospital and staff preparedness to deal with the COVID-19 crisis.

Vaccine hesitancy after taking the first dose of COVID-19 vaccine: A challenge for the COVID-19 vaccination program in India.

Background: Coronavirus disease 2019 (COVID-19) vaccination campaigns are trying to curb the pandemic by vaccinating as many individuals and as quickly as possible. The speed of immunization depends upon the availability of the vaccine and vaccine uptake by the communities, which in turn is related to vaccine hesitancy, the safety/efficacy profile of the vaccines, and adverse events following immunization (AEFI). Objectives: (i) To study the AEFI experienced by vaccine recipients and (ii) to assess the subjective effect of these AEFI on the vaccine recipients, that is, perceived disability and opinion regarding taking the vaccine's second dose. Methods: This was a cross-sectional study conducted at a tertiary care hospital where a questionnaire was distributed to the medical students who had taken at least one dose of a COVID-19 vaccine. Results: Out of 208 participants, more than three-quarters (n = 169, 81.2%) experienced AEFI symptoms within 12 hours of vaccination. The commonest symptoms were pain at the injection site (n = 173, 83.2%), body aches (n = 91, 43.8%), fever (n = 88, 42.3%), weakness (n = 86, 41.3%), and headache (n = 72, 34.6%). A majority of the participants reported complete recovery within 13-24 hours. Complete recovery was seen in all the study participants, and no serious event was seen. Twenty (10%) participants reported that they were not confident in taking the second dose of the vaccine. Conclusions: The disability perceived by the vaccine recipients should be taken into consideration in a vaccine with a multi-dose schedule. Pitfalls in alleviating the immunization-related anxiety should be identified and addressed.

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.

COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.

Genomic characterization unravelling the causative role of SARS-CoV-2 Delta variant of lineage B.1.617.2 in 2nd wave of COVID-19 pandemic in Chhattisgarh, India.

COVID-19 pandemic 2nd wave catastrophic effect in the state of Chhattisgarh, India, from where no exclusive genomic data yet published, has prompted us to undertake this study to unearth the causative variant. Whole-genome sequencing of SARS-CoV-2 isolated from COVID-19 infected nine vaccinated healthcare workers (HCW), thirty mild/moderate, seventeen severe, and twenty-seven deceased patients, was performed. The significant predominance of the SARS-CoV-2 variant of concern (VOC), Delta (lineage B.1.617.2) identified in sixty-four (77.1%) cases in contrast to B.1 and its sublineage in eleven (13.2%), variant under monitoring (VUM), Kappa (lineage B.1.617.1) in five (6.0%) and another VOC Alpha (lineage B.1.1.7) in three (3.6%) cases respectively (p < 0.05, χ2 = 162.49). 88.8% vaccine breakthrough, 60% mild/moderate, 94.4% severe and 81.5% dead patients were infected by Delta. Kappa presents exclusively in mild/moderate, Alpha in vaccine breakthrough, mild/moderate, and dead patient and B.1 and its sublineages in mild, severe, and dead patient categories. Delta variant spike mutation of T19R, G142D, E156G, L452R, and deletion (F157 and R158) helps in escaping antibody response, T478K and D614G enhance viral affinity with ACE2 receptor while P681R and D950N result in higher replication and transmissibility by cleaving S1/S2 at furin site. We conclude that Delta variant predominant role along with co-occurrence of Kappa, Alpha, and B.1 variant during COVID-19 2nd wave pandemic in Chhattisgarh may pose a potential threat of future outbreak through hybrid variant evolution. Thus, intensive genomic surveillance for monitoring variant evolution and a more efficacious vaccine against the Delta and Alpha variants are required.

Identification of bioactive molecule from Withania somnifera (Ashwagandha) as SARS-CoV-2 main protease inhibitor.

SARS-CoV-2 is the causative agent of COVID-19 and has been declared as pandemic disease by World Health Organization. Lack of targeted therapeutics and vaccines for COVID-2019 have triggered the scientific community to develop new vaccines or drugs against this novel virus. Many synthetic compounds and antimalarial drugs are undergoing clinical trials. The traditional medical practitioners widely use Indian medicinal plant Withania somnifera (Ashwagandha) natural constituents, called withanolides for curing various diseases. The main protease (Mpro) of SARS-CoV-2 plays a vital role in disease propagation by processing the polyproteins which are required for its replication. Hence, it denotes a significant target for drug discovery. In the present study, we evaluate the potential of 40 natural chemical constituents of Ashwagandha to explore a possible inhibitor against main protease of SARS-CoV-2 by adopting the computational approach. The docking study revealed that four constituents of Ashwagandha; Withanoside II (-11.30 Kcal/mol), Withanoside IV (-11.02 Kcal/mol), Withanoside V (-8.96 Kcal/mol) and Sitoindoside IX (-8.37 Kcal/mol) exhibited the highest docking energy among the selected natural constituents. Further, MD simulation study of 100 ns predicts Withanoside V possess strong binding affinity and hydrogen-bonding interactions with the protein active site and indicates its stability in the active site. The binding free energy score also correlates with the highest score of -87.01 ± 5.01 Kcal/mol as compared to other selected compounds. In conclusion, our study suggests that Withanoside V in Ashwagandha may be serve as a potential inhibitor against Mpro of SARS-CoV-2 to combat COVID-19 and may have an antiviral effect on nCoV.Communicated by Ramaswamy H. Sarma.

In silico identification of promising inhibitor against RNA-dependent RNA polymerase target of SARS-CoV-2.

The severe acute respiratory syndrome is a viral respiratory disease recognised as COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Formerly, no precise remedies are available, and many studies regarding COVID-19 prevention and treatment are under development. Several targets for the design of drugs are identified, and studies are in headway to explore the potential target. RNA-dependent RNA polymerase (RdRp) protein identified as a promising target against SARS-CoV-2 infection for the drug design due to its significant role in viral replication. The present study focuses on identifying the binding effect of previously known RdRp inhibitors with RdRp of SARS-CoV-2 using molecular docking and molecular dynamics simulation approaches. Molecular docking and binding free energy calculations against RdRp enzyme identified suramin as a potential compound that showed the highest docking score of -7.83 Kcal/mole and binding energy of -80.83 Kcal/mole as a comparison to other compounds. Further, molecular dynamics simulation studies were moreover showed the stable binding behaviour of suramin docked complex in the protein active site. Thus, the study concludes that suramin might be helpful as a potential inhibitor against RNA-dependent RNA polymerase of SRAS-CoV-2. However, further investigation is needed to assess the possible effect of inhibitors on RdRp through in vitro and in vivo experiments.

A novel scoring system for selecting the target patients of COVID-19 convalescent plasma therapy: A hypothesis.

The primary cause of mortality in patients of coronavirus disease 2019 (COVID-19) is the cytokine storm and not directly due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Therefore, it is being stressed by transfusion medicine specialists to use COVID-19 convalescent plasma (CCP) therapy early in the course of the disease, preferably within 72h of diagnosis. The authors herein, propose a scoring system for the rapid assessment of the patients who have tested positive for SARS-CoV-2. Therefore, a systematic approach may be followed where the patients are categorised into two groups, namely, the low-risk group [LRG; score<5] and the high-risk group [HRG; score ≥ 5] based on this scoring system. Those classified as an HRG should be administered CCP therapy within 72h of a confirmed diagnosis of COVID-19 to neutralise the SARS-CoV-2 virus and prevent the occurrence of the cytokine storm. This in turn could help reduce the overall mortality in the recipients.

Automated detection of COVID-19 from CT scan using convolutional neural network.

Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model's diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.

In silico identification of linear B-cell epitope in Coronavirus 2019 (SARS-CoV-2) surface glycoprotein: a prospective towards peptide vaccine

BACKGROUND: The 2020 Coronavirus pandemic continuing spread of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). At the moment, there is no specific antiviral treatment or monoclonal antibodies or vaccines available for COVID-19. SARS-CoV-2 is positive-stranded RNA viruses with a crown-like appearance due to the occurrence of spike (surface) glycoproteins on the envelope. In the present study, the computational method used to predict the significant linear B cell epitopes of SARS-CoV-2 surface glycoprotein. METHODS: FASTA sequence of SARS-CoV-2 surface glycoprotein was retrieved from the NCBI database, and further its primary and secondary structure was analyzed for its physical and chemicals properties. IEDB server was used to predict the B-cell epitopes. RESULTS: ABCprep server and IEDB server prediction results for B-cell epitopes showed 16 and 21 linear epitope sequences respectively in the surface glycoprotein of SARS-CoV-2. CONCLUSIONS: Obtained results conclude that predicted B-cell Epitopes may serve as an immunogen for eliciting monoclonal antibodies which can be used as a potential candidate for the treatment or diagnostic purpose for COVID-19.

SARS-CoV-2 antibody seroprevalence in India, August-September, 2020: findings from the second nationwide household serosurvey.

BACKGROUND: The first national severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serosurvey in India, done in May-June, 2020, among adults aged 18 years or older from 21 states, found a SARS-CoV-2 IgG antibody seroprevalence of 0·73% (95% CI 0·34-1·13). We aimed to assess the more recent nationwide seroprevalence in the general population in India. METHODS: We did a second household serosurvey among individuals aged 10 years or older in the same 700 villages or wards within 70 districts in India that were included in the first serosurvey. Individuals aged younger than 10 years and households that did not respond at the time of survey were excluded. Participants were interviewed to collect information on sociodemographics, symptoms suggestive of COVID-19, exposure history to laboratory-confirmed COVID-19 cases, and history of COVID-19 illness. 3-5 mL of venous blood was collected from each participant and blood samples were tested using the Abbott SARS-CoV-2 IgG assay. Seroprevalence was estimated after applying the sampling weights and adjusting for clustering and assay characteristics. We randomly selected one adult serum sample from each household to compare the seroprevalence among adults between the two serosurveys. FINDINGS: Between Aug 18 and Sept 20, 2020, we enrolled and collected serum samples from 29 082 individuals from 15 613 households. The weighted and adjusted seroprevalence of SARS-CoV-2 IgG antibodies in individuals aged 10 years or older was 6·6% (95% CI 5·8-7·4). Among 15 084 randomly selected adults (one per household), the weighted and adjusted seroprevalence was 7·1% (6·2-8·2). Seroprevalence was similar across age groups, sexes, and occupations. Seroprevalence was highest in urban slum areas followed by urban non-slum and rural areas. We estimated a cumulative 74·3 million infections in the country by Aug 18, 2020, with 26-32 infections for every reported COVID-19 case. INTERPRETATION: Approximately one in 15 individuals aged 10 years or older in India had SARS-CoV-2 infection by Aug 18, 2020. The adult seroprevalence increased approximately tenfold between May and August, 2020. Lower infection-to-case ratio in August than in May reflects a substantial increase in testing across the country. FUNDING: Indian Council of Medical Research.

Generalized SIR (GSIR) epidemic model: An improved framework for the predictive monitoring of COVID-19 pandemic.

Novel coronavirus respiratory disease COVID-19 has caused havoc in many countries across the globe. In order to contain infection of this highly contagious disease, most of the world population is constrained to live in a complete or partial lockdown for months together with a minimal human-to-human interaction having far reaching consequences on countries' economy and mental well-being of their citizens. Hence, there is a need for a good predictive model for the health advisory bodies and decision makers for taking calculated proactive measures to contain the pandemic and maintain a healthy economy. This paper extends the mathematical theory of the classical Susceptible-Infected-Removed (SIR) epidemic model and proposes a Generalized SIR (GSIR) model that is an integrative model encompassing multiple waves of daily reported cases. Existing growth function models of epidemic have been shown as the special cases of the GSIR model. Dynamic modeling of the parameters reflect the impact of policy decisions, social awareness, and the availability of medication during the pandemic. GSIR framework can be utilized to find a good fit or predictive model for any pandemic. The study is performed on the COVID-19 data for various countries with detailed results for India, Brazil, United States of America (USA), and World. The peak infection, total expected number of COVID-19 cases and thereof deaths, time-varying reproduction number, and various other parameters are estimated from the available data using the proposed methodology. The proposed GSIR model advances the existing theory and yields promising results for continuous predictive monitoring of COVID-19 pandemic.

Potential Inhibitors for SARS-CoV-2 and Functional Food Components as Nutritional Supplement for COVID-19: A Review.

The severe acute respiratory syndrome is a viral respiratory infection and commonly called as COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). It widely transmitted through direct or indirect contact. Currently, no specific treatment against SARS-CoV-2 are available; only prevention and supportive strategy are the preventive measures. The present review emphasizes the latest research related to COVID-19 and SARS-CoV-2 virus as well as the current status of potential inhibitors identified. Recent interest in SARS-CoV-2 has focused on transmission, symptoms, structure, and its structural proteins that exhibit promising therapeutics targets for rapid identification of potential inhibitors. The quick identification of potential inhibitors and immune-boosting functional food ingredients are crucial to combat this pandemic disease. We also tried to give an overview of the functional food components as a nutritional supplement, which helps in boosting our immune system and could be useful in preventing the COVID-19 and/or to improve the outcome during therapy.