Spread Of COVID-19 In India Research Articles

A fractional-order two-strain SVIR model with stability analysis

The outbreak and spread of infectious diseases has become a worldwide concern, and during the spread of infectious diseases, viruses have been found to evolve and diverge into different strains. Based on this phenomenon, the mathematical and biological properties of a multi-strain infectious disease model with vaccination have been studied. In this paper, a fractional-order two-strain SVIR model is proposed. Specifically, the model incorporates vaccine protection that follows a power-law tail distribution and where the vaccine affects the two-strain differently. And the fractional-order derivative form is given by derivation because its memory properties can better characterize the effect of vaccine protection. Meanwhile, we derive an explicit expression for the basic reproduction number of the two-strain and clarify its crucial role in governing the dynamics of the system. The stability results for the fractional-order model are given with the help of Routh–Hurwitz and Lyapunov methods. Moreover, it has been found that the spread of the two-strain of the infectious disease has different effects under different thresholds. Finally, the spread of COVID-19 in India is considered as a real case study to verify the rationality of the proposed model.

Examining the Relationship Between Digital Payment Adoption, Tourism Restrictions, and Public Health during pandemic

This study looks into how the ban on tourism, digital payments, and the spread of COVID-19 in India interact. With the use of a structural equation model that has been verified by confirmatory factor analysis, the study makes use of primary data collected from inhabitants of India using a five-point Likert scale. The results show a strong three-factor structural model with excellent fit and dependability. The main goal is to learn more about the public's knowledge and readiness for COVID-19 transmission control and evaluate how well digital payments and travel bans work to stop the virus's spread. In addition to providing empirical testing to clarify the untested model, the report highlights the critical roles that digital payment methods and tourism play in tackling the pandemic's issues. This study adds empirical support to the body of literature, illuminating the hitherto unexplored relationship between digital payments, tourism-related treatments, and their ability to lessen the effects of COVID-19. The findings offer a thorough grasp of the possible contributions of digital payment products and tourist rules in the larger framework of pandemic control, in addition to offering insightful information for policymakers.

Effectiveness of Learning Package on Impact of COVID-19 Pandemic on Physical, Mental, Social and Economic Status among Adults at Selected Villages of Gokak Taluk, Karnataka.

Context: As of march 11, 2021 more than 11 million people in India had been infected with COVID-19, and about 158,000 had died. The spread of COVID-19 in India is of great concern due to the country’s large and densely populated areas with widespread poverty and high migration rates, coupled with a high prevalence of chronic conditions that are generally poorly controlled.
 Objective: To develop and evaluate learning package to overcome from the impact of COVID-19 among adults.
 Setting and design: The study was conducted in selected villages of Gokak Taluk and pre-experimental research design was used.
 Materials and Methods: Population was comprised of adults aged between 18-60 years old. The sample size was 06 and simple random sampling technique was used to select the samples. Self constructed assessment scales were used. Learning package was developed and evaluated for its effectiveness.
 Statistical Analysis used: Descriptive and inferential statistics were used.
 Results: The pre-test mean score for functional status was 11.50 and SD was 2.429, anxiety status was 13.67 and SD was 2.685, stress status was 30.00 and SD was 2.898, social status was 26.50 and SD was 4.370 economic status was 5.67and SD was 0.816. In post-test mean score for functional status was 7.50 and SD was 2.258 with mean difference score of 4.00, anxiety status was 7.33 and SD was 1.366 with mean difference score of 6.33, stress status was 7.33 and SD was 0.81565 with mean difference score of 22.66, social status was 17.00 and SD was 2.098 with mean difference score of 9.50, economic status was 3.33 and SD was 0.516 with mean difference score of 2.33. The statistical paired ‘t’ implies that the difference in the pre test and post test score was found to be statistically significant at 5% level. There exists a statistical in the enhancement of score indicating the positive impact of learning package.
 conclusions: The impact of COVID-19 was more before distributing the learning package. Hence the learning package was effective.

Constructing the Muslim “other”: A critical discourse analysis of Indian news coverage of the tablighi jamaat congregation during the COVID-19 pandemic

This paper analyzes the representation of Indian Muslims in national news coverage during the COVID-19 pandemic. By deploying Critical Discourse Analysis (CDA), the paper examines how hegemonic news discourses in four Indian daily newspapers published in English and Hindi constructed the image of a Muslim “other” across various discursive moments in relation to the news coverage of a Tablighi Jamaat (TJ) congregation and its link to the spread of COVID-19 in India. Describing the congregation’s Muslim participants as anti-nationals, insensitive to the suffering of their compatriots, disrespectful of the law of the land, super-spreaders of the virus and jihadis, the image of the Muslim “other” was constructed and conveyed to the public by using such appellations. These conformed to many beliefs of Hindutva, the right-wing Hindu ideology, that seeks to infuse in public discourse feelings of the “other” – an other which needs to be criminalized and ostracized. The mediated reality produced is embedded in the power structure and is an ideologically driven exercise. The way hegemonic power operates is clearly reflected in the discourses about Muslims that circulated in the media during the first wave of COVID-19 in 2020.

Application of dynamic mode decomposition and compatible window-wise dynamic mode decomposition in deciphering COVID-19 dynamics of India

Abstract The COVID-19 pandemic recently caused a huge impact on India, not only in terms of health but also in terms of economy. Understanding the spatio-temporal patterns of the disease spread is crucial for controlling the outbreak. In this study, we apply the compatible window-wise dynamic mode decomposition (CwDMD) and dynamic mode decomposition (DMD) techniques to the COVID-19 data of India to model the spatial-temporal patterns of the epidemic. We preprocess the COVID-19 data into weekly time-series at the state-level and apply both the CwDMD and DMD methods to decompose the data into a set of spatial-temporal modes. We identify the key modes that capture the dominant features of the COVID-19 spread in India and analyze their phase, magnitude, and frequency relationships to extract the temporal and spatial patterns. By incorporating rank truncation in each window, we have achieved greater control over the system’s output, leading to better results. Our results reveal that the COVID-19 outbreak in India is driven by a complex interplay of regional, demographic, and environmental factors. We identify several key modes that capture the patterns of disease spread in different regions and over time, including seasonal fluctuations, demographic trends, and localized outbreaks. Overall, our study provides valuable insights into the patterns of the COVID-19 outbreak in India using both CwDMD and DMD methods. These findings can help public health organizations to develop more effective strategies for controlling the spread of the pandemic. The CwDMD and DMD methods can be applied to other countries to identify the unique drivers of the outbreak and develop effective control strategies.

Effectiveness of lockdown to mitigate the spread of COVID-19 pandemic during its first wave in India

Background: Countries across the globe had adopted various prevention and control measures ranging from social distancing and isolation, to shutting down of nonessential services and countrywide lockdowns. The objective of the study was to assess the effectiveness of the nation-wide lockdown to prevent or delay the spread of COVID-19 in India. Methods: This was a cross sectional and descriptive study. We used the secondary data from WHO website, from 01 January to 30 October 2020. We calculated mean, median number of new cases, deaths reported per day and case fatality rate, using Microsoft excel spreadsheet. Results: There was a steady rise in number of cases and deaths in India, during the lockdown and unlock period from phase-1 to phase-4. Approximately 10-fold increase in death was reported in the duration of 68 days of lockdown from phase-1 to Phase 4. The number of cases reported in unlock phase-4 were approximately 6 times more compared to unlock phase 1. The case fatality rate per day had declined during the lockdown period, from 3 .4% to 2 .5% deaths per day. The case fatality rate was lowest during the peak of pandemic, 1.27% in September 2020. Indicating good care of hospitalized patients compared to initial stage of pandemic in India. Conclusions: Indian government had imposed complete sudden lockdown at the right time. The peak of pandemic was delayed or slowed down. Indicating the timing of lockdown is an essential step to save the lives in pandemic, in resource limited settings.

Does Oxford Government Response Tracker Explain the SARS-CoV-2 Morbidity and Mortality: An Ecological Time Series Study.

Many nations swiftly designed and executed government policies to contain the rapid rise of SARS-CoV-2 cases. The government actions can be broadly segmented as movement and mass gathering restrictions (such as travel and lockdown), public awareness (such as facial covering and hand washing), emergency healthcare investment and social welfare provisions (such as poor welfare schemes to distribute food and shelter). The Blavatnik School of Government-Oxford university tracked various policy initiatives by governments across the globe and released them as composite indices. We assessed the overall government response using Oxford Comprehensive Health Index (CHI) and Stringency Index (SI) to combat the SARS-CoV-2 pandemic. This study aims to demonstrate the utility of CHI and SI to gauge and evaluate the government responses for containing the spread of SARS-CoV-2. We expect a significant inverse relationship between policy indices (CHI and SI) and SARS-CoV-2 severity indices (morbidity and mortality). In this ecological study, we analysed data from two publicly available data sources released between March 2020, to October 2021: Oxford Covid-19 Government Response Tracker (OxCGRT) and World Health Organization (WHO). We applied Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) to model the data. The performance of different models was assessed using a combination of evaluation criteria: Adj-R2, Root Mean Square of Error (RMSE) and Bayesian Information Criteria (BIC). The strict implementation of policies by the government to contain the crises of SARS-CoV-2 resulted in higher CHI and SI in the beginning. Although the value of CHI and SI gradually fell--the same was consistently higher at values of more than 80% points. During the initial investigation, we found that Cases Per Million (CPM) and Deaths Per Million (DPM) followed the same trend. However, the final CPM and DPM model were SARIMA (3,2,1)(1,0,1) and ARIMA (1,1,1), respectively. The current study does not support the hypotheses that SARS-CoV-2 severity (CPM and DPM) is associated with stringent policy measures (CHI and SI). Our study concludes that the policy measures (CHI and SI) do not explain the change in epidemiological indicators (CPM and DPM). The study reiterates our understanding that strict policies do not necessarily lead to better compliance but may overwhelm the overstretched physical health systems. The 21st-century problems, thus, demand 21st-century solutions. The digital ecosystem was instrumental in the timely collection, curation, cloud storage and data communication. Thus, digital epidemiology can--and--should be successfully integrated into existing surveillance systems for better disease monitoring, management and evaluation.

A new fractional mathematical model to study the impact of vaccination on COVID-19 outbreaks

This study proposes a new fractional mathematical model to study the impact of vaccination on COVID-19 outbreaks by categorizing infected people into non-vaccinated, first dose-vaccinated, and second dose-vaccinated groups and exploring the transmission dynamics of the disease outbreaks. We present a non-linear integer order mathematical model of COVID-19 dynamics and modify it by introducing Caputo fractional derivative operator. We start by proving the good state of the model and then calculating its reproduction number. The Caputo fractional-order model is discretized by applying a reliable numerical technique. The model is proven to be stable. The classical model is fitted to the corresponding cumulative number of daily reported cases during the vaccination regime in India between 01 August 2021 and 21 July 2022. We explore the sensitivities of the reproduction number with respect to the model parameters. It is shown that the effective transmission rate and the recovery rate of unvaccinated infected individuals are the most sensitive parameters that drive the transmission dynamics of the pandemic in the population. Numerical simulations are used to demonstrate the applicability of the proposed fractional mathematical model via the memory index at different values of 0.7,0.8,0.9 and 1. We discuss the epidemiological significance of the findings and provide perspectives on future health policy tendencies. For instance, efforts targeting a decrease in the transmission rate and an increase in the recovery rate of non-vaccinated infected individuals are required to ensure virus-free population. This can be achieved if the population strictly adhere to precautionary measures, and prompt and adequate treatment is provided for non-vaccinated infectious individuals. Also, given the ongoing community spread of COVID-19 in India and almost the pandemic-affected countries worldwide, the need to scale up the effort of mass vaccination policy cannot be overemphasized in order to reduce the number of unvaccinated infections with a view to halting the transmission dynamics of the disease in the population.

Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis

ObjectivesCOVID-19 has differentially affected countries, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of its spread. Vulnerability of a geographical region to COVID-19...

Modelling the first wave of COVID-19 in India.

Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. Here, we use a 9-component, age-stratified, contact-structured epidemiological compartmental model, which we call the INDSCI-SIM model, to analyse the first wave of COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to daily reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions (NPIs), an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05-0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 35% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

MODELING THE DYNAMICS OF COVID-19 TRANSMISSION IN INDIA: SOCIAL DISTANCING, REGIONAL SPREAD AND HEALTHCARE CAPACITY

In the new paradigm of health-centric governance, policymakers are in constant need of appropriate metrics to determine suitable policies in a non-arbitrary fashion. To this end, in this paper, a compartmentalized model for the transmission of COVID-19 is developed, with a socially distanced compartment added to the model. The modification allows for administrators to quantify the extent to which voluntary social distancing norms are followed, and address restrictions accordingly. Modifications are also made to incorporate inter-region migration, and suitable metrics are proposed to quantify the impact of migration on the rise of cases. The healthcare capacity is modeled and a method is developed to study the consequences of the saturation of the healthcare system. The model and related measures are used to study the nature of the transmission and spread of COVID-19 in India, and appropriate insights are drawn.

Consequences of pre and post confinement on the atmospheric air pollutants during spread of COVID-19 in India.

COVID-19, a severe respiratory syndrome, was diagnosed in Wuhan, China, and in the last week of January 2020, it was reported in India. The drastic speed of spreading of COVID-19 imposed a total lockdown in India for the first time in four stages. This leads to restrictions on transport, industries, coal-based power plants, etc. During these stages of lockdown, a detailed analysis was done to study the effect of confinement on various air pollutants, PM10, PM2.5, SO2, CO, NH3, and NOx (NO, NO2) over the thirteen different stations situated at different states in India. The data were compared with pre-confinement duration at different locations in India. During confinement, the air pollutants showed less value when compared with the pre-confinement stage alarming everyone and also the Indian government to bring up rules and regulations for better air quality index so that such pandemics should be reduced.

Financial Distress, COVID-19 and Listed SMEs: A Multi-methodology Approach

The multiple phase-wise lockdowns starting 24 March 2020, imposed to control COVID-19 spread in India, put a shrieking halt to industrial activities. Such nationwide lockdowns further exacerbated the financial distress, a long-standing challenge for small and medium-sized enterprises (SMEs). In this study, we examine how corporate governance forms like promoters’ ownership, financial performance and market competition affect the distress of listed SMEs, both in the pre-COVID-19 era and during the COVID-19 period. To do so, the study adopts a two-fold sampling and multi-methodology approach. First, by applying panel data analysis on the first sample of 80 listed SMEs for the financial year (FY), FY 2017–2018 to FY 2019–2020, pre-COVID-19 period results are obtained. Second, by applying cross-sectional analysis on the second sample of 155 listed SMEs for FY 2020–2021, results during the COVID-19 period are obtained. Main results indicate that previous years’ distress determines distress in the current year in the pre-COVID-19 era. In addition, promoters’ ownership and return on equity reduce the possibility of distress, while current period competition is insignificant. Conversely, COVID-19 can exacerbate distress and render promoters’ ownership insignificant. Furthermore, the COVID-19 period is characterized by reduced asset utilization. Therefore, any marginal increase in return on assets reduces distress. Interestingly, MC becomes an active determinant of distress during the COVID-19 pandemic. In summary, episodes like COVID-19 can modify the impact behaviour of the aforementioned determinants of distress. Notably, the results highlight the fragility of internal governance forms like ownership effects, while establishing the dominance of external factors like competition during the COVID-19 period.

An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown.

There have been considerable losses in terms of human and economic resources due to the current coronavirus pandemic. This work, which contributes to the prevention and control of COVID-19, proposes a novel modified epidemiological model that predicts the epidemic’s evolution over time in India. A mathematical model was proposed to analyze the spread of COVID-19 in India during the lockdowns implemented by the government of India during the first and second waves. What makes this study unique, however, is that it develops a conceptual model with time-dependent characteristics, which is peculiar to India’s diverse and homogeneous societies. The results demonstrate that governmental control policies and suitable public perception of risk in terms of social distancing and public health safety measures are required to control the spread of COVID-19 in India. The results also show that India’s two strict consecutive lockdowns (21 days and 19 days, respectively) successfully helped delay the spread of the disease, buying time to pump up healthcare capacities and management skills during the first wave of COVID-19 in India. In addition, the second wave’s severe lockdown put a lot of pressure on the sustainability of many Indian cities. Therefore, the data show that timely implementation of government control laws combined with a high risk perception among the Indian population will help to ensure sustainability. The proposed model is an effective strategy for constructing healthy cities and sustainable societies in India, which will help prevent such a crisis in the future.

Empirical evidence of effectsof stringency amid Covid-19 pandemic spread.

The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R-squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people’s lives in different sectors were also discussed in this paper.

E-NAM PLATFORM: A HAND TO SURVIVE INDIAN AGRICULTURE FROM COVID19 OUTBREAK

Coronavirus, also known as COVID19, was an unprecedented shock for the Indian economy, already in the vulnerability stage. With the prolonged country-wide lockdown, the Indian economy downturn. COVID19 had stuck almost all the sectors of the economy, but the agriculture sector had managed to survive in the COVID19 outbreak with the help of the Central Government scheme, namely the eNAM platform. This study aimed to evaluate the spread of COVID19 in India, the current status of the eNAM platform and its growth. In contrast, the study also focused on comparing the total commodity arrival of the eNAM integrated market during the COVID19 period with the previous period. The study had selected April 2020 to March 2021 as a COVID19 period and April 2019 to March 2020 as the last period; for comparison study had selected the Fathenager eNAM market as a sample. The particular research had applied the CARG model to evaluate the growth of eNAM markets, whereas for hypothesis testing implemented Chi-square test. eNAM had significantly increased growth by 84.8% in the last four years by integrating 1000 APMC markets with the eNAM platform.

Modeling and prediction of the third wave of COVID-19 spread in India

Abstract In this work, we proposed a simple SEIHR compartmental model to study and analyse the third wave of COVID-19 in India. In addition to the other features of the disease, we also consider the reinfection of recovered individuals in the model. For the purpose of parameter estimation we separate the infective and deaths classes and plot them against the cumulative counts of infective and deaths from data, respectively. The estimated parameters from these two are used for prediction and further numerical simulations.We note that the infective will keep on growing and only slow down after around three months. We have studied impact of various parameters on our model and observe that the parameters associated with mask usage, screening and the care giving toCOVID-19 patients have significant impact on the prevalence and time taken to slow down the infection.We conclude that better use of mask, effective screening and timely care to infective will reduce infective and can help in disease control. Our numerical simulations can explicitly provide a short term prediction for such time line. Also we note that providing better care facilities will help reducing peak as well as the disease burden of predicted infected cases.

Analysis of COVID-19 in India using a vaccine epidemic model incorporating vaccine effectiveness and herd immunity.

COVID-19 will be a continuous threat to human population despite having a few vaccines at hand until we reach the endemic state through natural herd immunity and total immunization through universal vaccination. However, the vaccine acts as a practical tool for reducing the massive public health problem and the emerging economic consequences that the continuing COVID -19 epidemic is causing worldwide, while the vaccine efficacy wanes. In this work, we propose and analyze an epidemic model of Susceptible–Exposed–Infected–Recovered–Vaccinated population taking into account the rate of vaccination and vaccine waning. The dynamics of the model has been investigated, and the condition for a disease-free endemic equilibrium state is obtained. Further, the analysis is extended to study the COVID-19 spread in India by considering the availability of vaccines and the related critical parameters such as vaccination rate, vaccine efficacy and waning of vaccine’s impact on deciding the emerging fate of this epidemic. We have also discussed the conditions for herd immunity due to vaccinated individuals among the people. Our results highlight the importance of vaccines, the effectiveness of booster vaccination in protecting people from infection, and their importance in epidemic and pandemic modeling.

Epidemiological modeling for COVID-19 spread in India with the effect of testing

A novel coronavirus has resulted in an outbreak of viral pneumonia in China. Person-to-person transmission has been demonstrated, but, to our knowledge, the spreading of novel coronavirus takes place due to an asymptomatic carrier. Most models are not considering testing and underlying network topology that shows the spreading pattern. By failing to integrate testing into the epidemiological model, models missed a vital opportunity to better understand the role of asymptomatic infection in transmission. In this work, we propose a model considering testing as well as asymptomatic infection considering underlying network topology. We extract the transmission parameters from the data set of COVID-19 of India and apply those parameters in our proposed model. The simulation results support our theoretical derivations, which show the impact of testing and asymptomatic carrier in infection spreading.

Hookah smoking and COVID-19 in India

In recent years, hookah smoking has gained popularity in India, especially among youth. However, its abuse increases the risk of COVID-19. The present letter discusses the harmful effects of hookah smoking, international policies for the cessation of hookah smoking, and the pressing need to have policies to mitigate hookah use and curb the spread of COVID-19 in India.