COVID-19 Outbreak In India Research Articles

Exploring Air Quality Dynamics and Predictive Modeling by Using Artificial Intelligence During COVID-19 Lock Down Over the Western Part of India

The lockdown period, initially imposed for three months due to the COVID-19 outbreak in India, was later prolonged. Air quality data from eight monitoring sites in Rajasthan was used to calculate the AQI according to the following parameters: Particulate matter (PM2.5 and PM10), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulfur dioxide (SO2), Ozone (O3), and Carbon monoxide (CO), dispersed throughout the state by CPCB. Among the chosen cities, the study found that the AQI percentage dropped the most in Alwar, by 35.6% between pre-lockdown and lockdown. Conversely, it rose the most in Jaipur, by 86.77% between lockdown and post-lockdown. Python deep learning was used to simulate the relationship between Air Quality Index and Air contamination in the study area. Air quality index values ranging from Good (0–50) to Severe (>401) were used to create the AQI class categorization in Python. The study found that PM2.5 and PM10 had the strongest correlation. Metrics such as the coefficient of determination (R2) and the root mean square error (RMSE) were applied to assess the model on the datasets used for training and testing. Random forest, decision trees, and linear regression were worked to verify the precision of the prototype. The author used supervised learning techniques, such as decision tree (DT), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), logistic regression (LR), and random forest (RF), to determine the model's prediction. These findings suggest that urban areas are characterized by societal, commercial, and cultural aspects that contribute to similar discharge patterns and air quality issues. The study would be advantageous for authorities, as it is clearly apparent that reducing the sources of emissions can improve quality. This will set the stage for safeguarding and improving the environment.

COVID-19 OUTBREAK IMPACT ON THE INDIANS

The impact of the COVID-19 outbreak in India, though possibly surpassing its peak caseload in 2023, continues to have a profound effect on the nation's health, society, and economy. While the public health system managed the initial surge, it now faces ongoing challenges due to resource and personnel strain. The heightened demands during the peak may have resulted in neglect of preventative and diagnostic care for non-COVID ailments. Socioeconomic gaps widened as the pandemic disproportionately affected informal workers and low-income communities, pushing many further into poverty through lockdowns, business closures, and job losses. The lasting economic consequences are still unfolding, potentially leading to a decline in GDP and investment. Mental health emerged as a significant concern, with lockdowns, social isolation, and fear contributing to increased depression, anxiety, and other issues. Although India has made progress in curbing the virus's spread, achieving full recovery will likely be a prolonged process requiring sustained efforts to bolster the healthcare system, address socioeconomic disparities, and offer mental health support to the population.

The role of smart cities in managing the COVID-19 outbreak in India

The role of smart cities in managing the COVID-19 outbreak in India

Embodied Uncertainty and COVID-19: Social identity, stigma, trust and coping through the pandemic

The COVID-19 pandemic has been a novel and evolving experience impacting health, livelihoods, well-being, decision-making, and community life. While uncertainty is seen as integral to the pandemic experience, limited literature has examined the consequences of the same, as embodied within individual and collective experiences. From the theoretical lens of Embodied Uncertainty (Sword-Daniels et al. 2018), the present study explored meaning making and coping with uncertainty as embodied in the lived experiences of the pandemic, through longitudinal qualitative research (LQR). Participants from a diverse socio demographic pool were interviewed in 2020 (N = 30) and 2021 (N = 14), following the two major phases of Covid-19 outbreak in India. Thematic analysis revealed complex intersections between social identity, stigma and economic strife and how novel coping strategies were being employed as the ongoing nature of the pandemic became a reality. We also found shifts in information seeking and institutional trust, shaped by the lived experiences of people as opposed to policy and state action. The results highlight key areas of vulnerability and coping with uncertainty in collective crises, gaps in state response and its impact, and how social identities shape interpretations of risks.

Predicting COVID-19 outbreak in India using modified SIRD model

In this paper, the existing Susceptible-Infected-Recovered-Deceased (SIRD) compartmental epidemiologic process model is modified for forecasting the coronavirus effect in India. The data from India was studied for weekly fatalities, weekly infected, weekly recovered, new cases, infected and recovered individuals, Reproductive Number R 0 , recovery rate, death rate, and coefficient of transmission from 30 January 2020 to 31 July 2021. SARS Coronavirus 2 (SARS-CoV-2) is the Covid strain that causes Covid sickness (COVID-19), a respiratory ailment that triggered the outbreak of COVID-19 at the beginning of December 2019. We aim to provide a hybrid SIRD model for predicting the COVID-19 outbreak. In the proposed method, to improve the exploration ability of the Grey Wolf Optimizer (GWO) or to avoid stagnation in the swarm, a modified Grey Wolf Optimization Algorithm is used to optimize the initial value of Infected individuals. The modified SIRD model is further applied to get the predicted values. The data is examined on weekly basis to prevent noise. Depending on the fact, that the precise mode of transmission is highly dependent on how and when different precautions such as isolation, confinement, and other preventative measures were implemented, we put together our projections concerning satisfactory speculations based on genuine realities. The experimental results show the various trends observed in the pandemic in terms of number of peaks, increasing trend, decreasing trend, and continuous trend for infected individuals, weekly change in number of cases, weekly deaths, weekly infected, and weekly recoeverd cases of Covid-19. The proposed modified SIRD model could be a valuable tool for assessing the impact of government measures on COVID-19 outbreak.

An Analysis on Domestic Violence, Depression and Surge of Anger During the COVID-19 Outbreak in India

Covid-19, which is also referred to as Novel Coronavirus Disease, has begun in China and has rapidly crossed multiple national borders and infected people all over the world, where India has been severely affected by this outbreak in almost all sectors; as a result of the threat of the spread of this viral disease, India, at the beginning, has been forced to shut down all general activities and to force a tight lockdown to restrict human interactions. These sudden restrictions and orders have puzzled and affected the common public in many ways, such as emotionally, physically, financially, and career-wise, halting their potential improvements, and have also ruined many people's livelihoods and the rhythm of life in India. Although the process of unlocking has started and successfully implemented, at the time of post lockdown, people were not afraid of the coronavirus at the same time, due to the experiences of facing rapid attacks from new variants such as Delta, Delta Plus, and Omicron, triggered different psychological and social problems in the living society. In this connection, this research paper exclusively focuses on the sudden rise of domestic violence, depression, and surge of anger between people since the virus outbreak in India. This study covers various associated issues as well as views on financial and social issues and government actions to control the spread of the virus when these negative characteristics make the situation more vulnerable. Thus, through the findings and analysis of this paper, it is useful to know the pulse of common people in India and their tactics to handle these situations which also prevents negativity in people because of these crucial circumstances.

DECIPHERING DYNAMICS OF COVID-19 OUTBREAK IN INDIA: AN AGE-STRUCTURED MODEL

The dynamics of infectious disease transmission depends on social contact patterns and the precautions people take to minimize disease transmission. The social contact pattern varies depending on the community at large age distribution at work, school, and recreation. Consequently, knowing the age-specific prevalence and incidence of infectious diseases is critical for predicting future disease burden and the efficacy of interventions like vaccination. In this study, we use an SEIR age-structured multi-group epidemic model to understand how social contact affects disease control. We construct location-specific social contact matrices to observe that how social mixing affects disease spread. For mathematical analysis, we compute the basic reproduction number [Formula: see text] and exhibit the global behavior of the system in terms of [Formula: see text]. We also estimate the transmission rate using the empirical data of COVID-19 for India from 1 September 2020, to 31 December 2020. We simulate the lifting of non-pharmaceutical interventions by allowing people to return to work in phases and investigate its impact. Our findings imply that raising awareness in symptomatic infected individuals aged 20–49 years can help in reducing the number of infected individuals. When some schools are partially open, awareness in symptomatic infected individuals in school-age groups also plays a significant role in reducing disease cases. The simulation results also suggest that limiting contact at school and other gathering places could significantly lower the number of instances. Using the least square approach, we show that the time-dependent transmission rate is more realistic than the constant spread rate for COVID-19 in India. To reduce the COVID-19 burden, we obtain that gradually loosening control measures could flatten and lower other peaks. Our findings may help health policymakers to decide on timely age-based vaccination distribution strategies and control measures.

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.

Deciphering Predictors of Tourists’ Value and Intention amid COVID-19: The Interplay of Scarcity, Enjoyment, Visual Presentations, and Pandemic Threat

The study investigates the factors influencing tourists’ online booking intentions. This study employed structural equation modeling to evaluate the online booking intentions of tourists after the second wave of the COVID-19 outbreak in India, extending the application of reactance theory in tourism and hospitality services. Mediation and moderation analysis facilitated the unfurling of direct and indirect linkages among the constructs. The findings suggest that online hotel reviews, perceived scarcity, and perceived enjoyment aggrandize tourists’ perceived value quotients, escalating their online booking intentions. Besides, visual presentations improve the strength of the linkage, while perceived pandemic threat weakens the linkage between tourists’ perceived value and online booking intentions. The research demystifies critical facilitators of tourists’ perceived value and online booking intentions that may assist hotel owners in engaging potential tourists and maximizing their hotels’ bottom line. The hotel industry lacks research on perceived scarcity, visual presentations, and perceived pandemic threat. This research adds to the body of knowledge by combining the abovementioned factors through a coherent theoretical framework. It makes prospective tourists cautious about COVID-19’s virulence and the devastating consequences.

Patterns in Child Health Outcomes Before and After the COVID-19 Outbreak in India

This cross-sectional study examines prepandemic-to-postpandemic changes in mortality, nutrition and feeding practices, anthropometry, vaccination, and other measures in a sample of children from the Indian National Family Health Survey.

Assessing potential insights of an imperfect testing strategy: Parameter estimation and practical identifiability using early COVID-19 data in India

A deterministic model with testing of infected individuals has been proposed to investigate the potential consequences of the impact of testing strategy. The model exhibits global dynamics concerning the disease-free and a unique endemic equilibrium depending on the basic reproduction number when the recruitment of infected individuals is zero; otherwise, the model does not have a disease-free equilibrium, and disease never dies out in the community. Model parameters have been estimated using the maximum likelihood method with respect to the data of early COVID-19 outbreak in India. The practical identifiability analysis shows that the model parameters are estimated uniquely. The consequences of the testing rate for the weekly new cases of early COVID-19 data in India tell that if the testing rate is increased by 20% and 30% from its baseline value, the weekly new cases at the peak are decreased by 37.63% and 52.90%; and it also delayed the peak time by four and fourteen weeks, respectively. Similar findings are obtained for the testing efficacy that if it is increased by 12.67% from its baseline value, the weekly new cases at the peak are decreased by 59.05% and delayed the peak by 15 weeks. Therefore, a higher testing rate and efficacy reduce the disease burden by tumbling the new cases, representing a real scenario. It is also obtained that the testing rate and efficacy reduce the epidemic’s severity by increasing the final size of the susceptible population. The testing rate is found more significant if testing efficacy is high. Global sensitivity analysis using partial rank correlation coefficients (PRCCs) and Latin hypercube sampling (LHS) determine the key parameters that must be targeted to worsen/contain the epidemic.

Islamophobic Discourses in India During COVID-19 Pandemic: A Case of Tablighi Jamaat

ABSTRACT Islamophobia is a hot issue in the world nowadays. This study aimed to analyse the discourses which were produced against Muslims and Islam during the novel coronavirus outbreak in India, where the Muslim minority group Tablighi Jamaat was targeted and held responsible for spreading the virus. The study employed Critical Discourse Analysis to analyse selected tweets from Indian politicians, government dignitaries, and common Indian users to find out Islamophobic themes and ideological structures in their discourses. The researcher hypothesizes that Islamophobia is an integral feature of Indian political communication which is also obvious in their discursive and rhetorical devices. The researcher purposively selected 50 of the most controversial tweets by Indian politicians, government officials, and common Indian Twitter users against Muslims and Islam and carried out a critical discourse analysis through political discourse strategies by Wodak’s (2014). The findings of the study reveal that Indians use discriminatory language against Muslims and Islam to create a difference of “us versus them” and by using anti-Islamic strategies and Islamophobic remarks against the Muslims of India and propagating a common belief in Indians that Muslims and Islam are the main culprit of the COVID-19 outbreak in India.

Status of mental health among college and university students during first and second wave of COVID-19 outbreak in India: A cross-sectional study.

India has been badly affected by Covid-19 not only in terms of human lives but also has a long-term effect on mental health of the population. This paper is an attempt to understand the psychological effects of the pandemic on the college and university students in India after the second wave of COVID-19 outbreak and its associated factors. A web-based survey was conducted to collect information from the students both at individual and household level. The study applied binary and multivariate logistic regression to estimate the adjusted and unadjusted marginal effects of the predictor variables. Results show a significant increase in mental health concerns during the second wave of the pandemic, as compared to the first year. The key factors contributing to the higher prevalence of depression, anxiety, and stress are the difficulties faced in the adaptation to the new way of learning, fear of discontinuation of education due to financial constraints faced by household, limited physical interaction, and prolonged screen-time during the pandemic. The study has some limitations regarding selection of the sample as the survey was web-based. Also, the mental health situation of the students is self-reported and the study does not consider the prevailing mental health issues before the pandemic. The study recommends initiatives like offering counselling classes and strategies to cope up with mental health disorders. Further, there is a need to conduct follow-up studies to assess the long-term impacts of prolonged home quarantine on the mental health of the students.

Economic geography of contagion: a study of COVID-19 outbreak in India.

The online version contains supplementary material available at 10.1007/s00148-022-00935-9.

Apprehensions and Optimism among Dental Professionals during the COVID-19 Pandemic-A Nationwide Cross-Sectional Evaluation.

Globally, the pandemic of the coronavirus disease, which started in Wuhan, China, has become a major issue for public health. The COVID-19 epidemic notably causes health professionals to experience significantly more emotional stress than the general populace. The present study proposes to investigate the fear aspect in dentists in the initiation of clinical practice during these times. An online cross-sectional study was conducted among dental practitioners based on a pre-validated questionnaire. The data were expressed as frequency and percentage analyzed using the chi-square test using SPSS version 25. The data was obtained from 271 participants, where clinical practice after the first wave was mostly by freelancers (p-value = 0.01); most of whom were married (p-value = 0.065); 19.1% attached to institutes did not have changes in earnings; 28.1% of private practitioners had less than 10% reduction in cases. A total of 62% of private practitioners are concerned about the vaccine's preventative effects (p-value = 0.026), and 57% of private practitioners worry about being sued for the delay in treatment (p-value = 0.036). Only 33.1% of employees in institutions worry that becoming sick could endanger their family. As dentists continue to work their way through this pandemic, these pressures only occasionally have an impact on them. According to the researchers, this is the first study that has captured the anxiety and apprehensions that dental practitioners experienced during the height of the COVID-19 outbreak in India in April 2021. The study's findings demonstrate that the study population was generally upbeat and confident that they could quickly overcome their fear.

RETHINKING COVID-19 OUTBREAK IMPACT

The world is going through a tough time and struggling with a pandemic COVID-19. An illness started from Wuhan city, now spread over almost all countries of the globe. Vaccination process is still going on and people will have to wait a long for complete eradication. Till then the only prevention is following social distancing, wearing mask, being in quarantine and isolation. World leaders are imposing lockdown in the countries with different intensity. In totality this is a challenging time, not just for the leaders, doctors, and police but for the normal people including private employees, daily wage earners and self employed individuals. Economic activities got a long halt. Every part of the population is getting affected. Emotions of stress, anxiety, anger, fear and frustration are obvious among people in this lockdown time. During this time Indian spirituality has been rebooted by many aspects. There is no doubt that India is the richest in its knowledge of Ayurveda, Ethos and Universe. Keeping this in mind existing literature of COVID-19 outbreak with special context to call for mental health and role of spiritual practices in mitigating the stress was searched with the help of different database. Published articles were represented in systematic form and summarize based on their methodologies and themes. So, this paper is trying to give a picture how with the help of Indian spiritual practices people can regain their faith and self-confidence since Indian Government is doing a lot with Ayurvedic preventions and connecting people with knowledge and peace by re telecasting great Indian tales like Ramayana, Mahabharata and Upanishad. Objectives of the study: Following are the main objectives of this studyŸ To investigate COVID-19 outbreak in India and its general impact on business and human. Ÿ To study re conceptualization of business strategies during and after lockdown. Ÿ To investigate need of mental health call in pandemic. Ÿ To understand role of spirituality in stress management during COVID-19 outbreak.

An SIR Model for COVID-19 Outbreak in India

In this paper, we have proposed a SIR fuzzy epidemic model by taking the transmission rate and recovery rate as fuzzy numbers. The basic reproduction number and the fuzzy basic reproduction number have been computed. Further by considering the initial values for the susceptible, infected and recovered population the numerical simulation has been carried out using Runge-Kutta method. We can predict the transmission of the virus and prevent the COVID-19 outbreak in India with the results obtained from the proposed SIR model.

Data Science Methods to Develop Decision Support Systems for Real-time Monitoring of COVID-19 Outbreak

Data analysis and visualization are essential for exploring and communicating findings in medical research, especially in epidemiological surveillance. Data on COVID-19 diagnosed cases and mortality from crowdsourced website COVID-19 India Tracker, Census 2011, and Google Mobility reports has been used to develop a real-time analytics and monitoring system for the COVID-19 outbreak in India. We have developed a dashboard application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using data science techniques. A district-level tool for basic epidemiological surveillance, in an interactive and user-friendly manner, which includes time trends, epidemic curves, and key epidemiological parameters such as growth rate, doubling time, and effective reproduction number, has been estimated. This demonstrates the application of data science methods and epidemiological techniques in public health decision-making while addressing the gap of timely and reliable decision-aiding tools. Doi: 10.28991/HEF-2022-03-02-08 Full Text: PDF

Breaking the silos, stopping the spread: an interview with Jyoti Joshi.

Breaking the silos, stopping the spread: an interview with Jyoti Joshi.

Successful application of wastewater-based epidemiology in prediction and monitoring of the second wave of COVID-19 with fragmented sewerage systems-a case study of Jaipur (India).

The present study tracked the city-wide dynamics of severe acute respiratory syndrome-corona virus 2 ribonucleic acids (SARS-CoV-2 RNA) in the wastewater from nine different wastewater treatment plants (WWTPs) in Jaipur during the second wave of COVID-19 out-break in India. A total of 164 samples were collected weekly between February 19th and June 8th, 2021. SARS-CoV-2 was detected in 47.2% (52/110) influent samples and 37% (20/54) effluent samples. The increasing percentage of positive influent samples correlated with the city’s increasing active clinical cases during the second wave of COVID-19 in Jaipur. Furthermore, wastewater-based epidemiology (WBE) evidence clearly showed early detection of about 20 days (9/9 samples reported positive on April 20th, 2021) before the maximum cases and maximum deaths reported in the city on May 8th, 2021. The present study further observed the presence of SARS-CoV-2 RNA in treated effluents at the time window of maximum active cases in the city even after tertiary disinfection treatments of ultraviolet (UV) and chlorine (Cl2) disinfection. The average genome concentration in the effluents and removal efficacy of six commonly used treatments, activated sludge process + chlorine disinfection (ASP + Cl2), moving bed biofilm reactor (MBBR) with ultraviolet radiations disinfection (MBBR + UV), MBBR + chlorine (Cl2), sequencing batch reactor (SBR), and SBR + Cl2, were compared with removal efficacy of SBR + Cl2 (81.2%) > MBBR + UV (68.8%) > SBR (57.1%) > ASP (50%) > MBBR + Cl2 (36.4%). The study observed the trends and prevalence of four genes (E, RdRp, N, and ORF1ab gene) based on two different kits and found that prevalence of N > ORF1ab > RdRp > E gene suggested that the effective genome concentration should be calculated based on the presence/absence of multiple genes. Hence, it is imperative to say that using a combination of different detection genes (E, N, RdRp, & ORF1ab genes) increases the sensitivity in WBE.Graphical abstract