COVID-19 Cases In India Research Articles

IJCM_167A: A cross sectional study on the preparedness and mitigation strategies for the second wave of COVID-19 among health science students in Mangalore

Background: The second wave of COVID-19 severely affected the healthcare workforce, including doctors, nurses, public servants, and medical students, even though the system was better prepared than the first wave. Objectives: To assess the preparedness and preventive measures of the health science students towards the second wave of COVID-19. Methodology: The study has been carried out in Kasturba Medical College (KMC) and Manipal College of Dental Sciences (MCODS), Mangalore, after getting approval from the institutional ethics committee (IEC) and concerned deans of institutions. The responses to the questionnaires have been taken in for analysis which constitutes three parts. The chi-square test was applied to compare the variables between medical and other health sciences students considering the significant P value less than 0.05. Results: The overall readiness regarding the surge in COVID-19 cases in India among our study participants was 99.3% which was highest among AHS students. Many of the students in the study showed passion and were prepared to assist health professionals at Health and Triage centres, with the highest distribution in AHS (97.2%), BDS (87%), and MBBS (85.8%). They believed that adequate protective gear and infection testing were needed for an effective return to the clinical setting. Conclusion: Ultimately, the health science student population in the study settings possesses sufficient resources and knowledge to combat the second wave of COVID-19. However, a certain shortcoming remains in the form of laxity toward preventive measures and social exclusion.

Mathematical modelling of COVID-19 dynamics using SVEAIQHR model

Abstract In this study, we formulate an eight-compartment mathematical model with vaccination as one of the compartments to analyze the dynamics of COVID-19 transmission. We examine the model’s qualitative properties, such as positivity and boundedness of solutions, and stability analysis of the illness-free equilibrium with respect to the basic reproduction number. We estimate ten significant parameters and also compute the magnitude of the basic reproduction number for India by fitting the proposed model to daily confirmed and cumulative confirmed COVID-19 cases in India. Sensitivity analysis with respect to basic reproduction number is conducted, and the main parameters that impact the widespread of disease are determined. We further extend this model to an optimal control problem by including four non-pharmaceutical and pharmaceutical intervention measures as control functions. Our numerical results show that the four control strategy has greater impact than the three control strategies, two control strategies, and single control strategies on reducing the dynamics of COVID-19 transmission.

Tracing the COVID-19 spread pattern in India through a GIS-based spatio-temporal analysis of interconnected clusters

Spatiotemporal analysis is a critical tool for understanding COVID-19 spread. This study examines the pattern of spatial distribution of COVID-19 cases across India, based on data provided by the Indian Council of Medical Research (ICMR). The research investigates temporal patterns during the first, second, and third waves in India for an informed policy response in case of any present or future pandemics. Given the colossal size of the dataset encompassing the entire nation’s data during the pandemic, a time-bound convenience sampling approach was employed. This approach was carefully designed to ensure a representative sample from advancing timeframes to observe time-based patterns in data. Data were captured from March 2020 to December 2022, with a 5-day interval considered for downloading the data. We employ robust spatial analysis techniques, including the Moran’s I index for spatial correlation assessment and the Getis Ord Gi* statistic for cluster identification. It was observed that positive COVID-19 cases in India showed a positive auto-correlation from May 2020 till December 2022. Moran’s I index values ranged from 0.11 to 0.39. It signifies a strong trend over the last 3 years with r^2 of 0.74 on order 3 polynomial regression. It is expected that high-risk zones can have a higher number of cases in future COVID-19 waves. Monthly clusters of positive cases were mapped through ArcGIS software. Through cluster maps, high-risk zones were identified namely Kerala, Maharashtra, New Delhi, Tamil Nadu, and Gujarat. The observation is: high-risk zones mostly fall near coastal areas and hotter climatic zones, contrary to the cold Himalayan region with Montanne climate zone. Our aggregate analysis of 3 years of COVID-19 cases suggests significant patterns of interconnectedness between the Indian Railway network, climatic zones, and geographical location with COVID-19 spread. This study thereby underscores the vital role of spatiotemporal analysis in predicting and managing future COVID-19 waves as well as future pandemics for an informed policy response.

Early Death Among COVID-19 Patients: A Cross-sectional Analysis of the First 10,000 COVID-19 Deaths from the Indian State of Tamil Nadu

BackgroundTamil Nadu state reported the second highest number of confirmed COVID-19 cases in India. In this study, we aimed to describe and determine the risk factors for early death among the first 10,000 COVID-19 deaths in the state.MethodsWe conducted a cross sectional analysis of state government administrative data to describe deaths, examine the differences between early deaths and non-early deaths, and calculate the risks of early death for several independent variables. All p-values < 0.05 were considered statistically significant.ResultsIn total, 4147 early deaths (41.5%) were recorded; the median age of patients who suffered from early death was significantly lower [64 years; interquartile range (IQR): 55–72] when compared with patients who did not suffer from early death (65 years; IQR: 56–73). After adjusting for comorbidities, age, and the time elapsed from the onset of symptoms to hospitalization; we found that the risk of early death was significantly lower for males [adjusted odds ratio (aOR): 0.82; 95% confidence interval (CI): 0.72, 0.93; p = 0.002], among rich individuals (aOR: 0.76; 95% CI: 0.63, 0.92; p = 0.004), in the richest districts (aOR: 0.70; 95% CI: 0.59, 0.84; p < 0.001) and for those who received treatment in private facilities (aOR: 0.45; 95% CI: 0.40, 0.51; p < 0.001.ConclusionsThe risk of early deaths among the first 10,000 reported COVID deaths in the Tamil Nadu state of India was higher in patients treated in government hospitals especially in the poorest districts probably indicating a lack of infrastructure in government facilities or the overburdening of government facilities at least in the early phase of the pandemic.

Chasing SARS-CoV-2 XBB.1.16 Recombinant Lineage in India and the Clinical Profile of XBB.1.16 Cases in Maharashtra, India.

Background SARS-CoV-2 has evolved rapidly, resulting in the emergence of lineages with a competitive advantage over one another. Co-infections with different SARS-CoV-2 lineages can give rise to recombinant lineages. To date, the XBB lineage is the most widespread recombinant lineage worldwide, with the recently named XBB.1.16 lineage causing a surge in the number of COVID-19 cases in India. Methodology The present study involved retrieval of SARS-CoV-2 genome sequences from India (between December 1, 2022 and April 8, 2023) through GISAID; sequences were curated, followed by lineage and phylogenetic analysis. Demographic and clinical data from Maharashtra, India were collected telephonically, recorded in Microsoft® Excel, and analyzed using IBM® SPSS statistics, version 29.0.0.0 (241). Results A total of 2,944 sequences were downloaded from the GISAID database, of which 2,856 were included in the study following data curation. The sequences from India were dominated by the XBB.1.16* lineage (36.17%) followed by XBB.2.3* (12.11%) and XBB.1.5* (10.36%). Of the 2,856 cases, 693 were from Maharashtra; 386 of these were included in the clinical study. The clinical features of COVID-19 cases with XBB.1.16* infection (XBB.1.16* cases, 276 in number) showed that 92% of those had a symptomatic disease, with fever (67%), cough (42%), rhinorrhea (33.7%), body ache (14.5%) and fatigue (14.1%) being the most common symptoms. The presence of comorbidity was found in 17.7% of the XBB.1.16* cases. Among the XBB.1.16* cases, 91.7% were vaccinated with at least one dose of vaccine against COVID-19. While 74.3% of XBB.1.16* cases were home-isolated; 25.7% needed hospitalization/institutional quarantine, of these, 33.8% needed oxygen therapy. Out of 276 XBB.1.16* cases, seven (2.5%) cases succumbed to the disease. The majority of XBB.1.16* cases who died belonged to an elderly age group (60 years and above), had underlying comorbid condition/s, and needed supplemental oxygen therapy. The clinical features of COVID-19 cases infected with other co-circulating Omicron variants were similar to XBB.1.16* cases. Conclusion The study reveals that XBB.1.16* lineage has become the most predominant SARS-CoV-2 lineage in India. The study also shows that the clinical features and outcome of XBB.1.16* cases were similar to those of other co-circulating Omicron lineage infected cases in Maharashtra, India.

Influence of fear on purchase of health insurance

At the start of the year 2020, World Health Organization declared a public health emergency of international concern about “The COVID-19 pandemic”. During this pandemic, when the entire world was facing lockdown, and all industries were getting a setback, there was a spurt observed in the insurance industry. With the rise in COVID-19 cases in India, it became a wake-up call for people towards health insurance. Health Insurance is one of the critical elements for sustainable and inclusive development. A paradigm shift has been noticed among individuals towards health insurance during this pandemic time, especially to protect their families from unforeseen medical expenses. This paper identifies the impact of COVID-19 as a fear that leads to a change in behaviour towards health insurance products. This study is based on the primary data collected from Ahmedabad city showing behaviour towards purchasing an insurance product during the pandemic situation. A sample of 200 individuals from Ahmedabad city of Gujarat State in India has been surveyed. The Binary logistic model is used to analyse the significant factors for the purchase of health insurance and also to check whether fear has a significant impact on a purchase decision.

Estimation of real COVID-19 cases in India during the first wave

In 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread across the world within a few months. The SARS-CoV-2 pandemic has had a devastating effect on humanity, with social and economic consequences. The continents of Europe and America have been hit the hardest. However, there has also been a huge loss of life in India, with the country having the fourth highest number of total deaths worldwide. Nevertheless, the infection and death rates per million and the case fatality ratio in India are substantially lower than those in many developed nations. Several explanations for this have been proposed, including genetics. Mathematical modelling has suggested that the actual number of infections is much higher than the number of reported cases. Therefore, to understand the dynamics of actual infection and the population-level immunity against SARS-CoV-2, a serosurvey (antibody testing) was performed among 2301 individuals in urban regions of 14 districts in six states of India. A notable outcome of this study was that a large proportion of the Indian population had an asymptomatic SARS-CoV-2 infection. The real infection rate in India was several fold higher than the reported number of cases. Therefore, a large number of people in the country have developed SARS-CoV-2-specific antibodies. In this survey, the seroprevalence (frequency antibody-positive) varied between 0.01 (95% CI 0.002–0.054) and 0.477 (95% CI 0.392–0.563), suggesting a high variability in viral transmission between the states and the possibility of future waves. In this study population, the frequency of asymptomatic infection was highest in the younger age groups. It was also found that the numbers of cases reported by the government were several-fold lower than the real incidence of infection. It is likely that the high number of asymptomatic cases was the main driver of this discrepancy.

Computational modeling of fractional COVID-19 model by Haar wavelet collocation Methods with real data.

This study explores the use of numerical simulations to model the spread of the Omicron variant of the SARS-CoV-2 virus using fractional-order COVID-19 models and Haar wavelet collocation methods. The fractional order COVID-19 model considers various factors that affect the virus's transmission, and the Haar wavelet collocation method offers a precise and efficient solution to the fractional derivatives used in the model. The simulation results yield crucial insights into the Omicron variant's spread, providing valuable information to public health policies and strategies designed to mitigate its impact. This study marks a significant advancement in comprehending the COVID-19 pandemic's dynamics and the emergence of its variants. The COVID-19 epidemic model is reworked utilizing fractional derivatives in the Caputo sense, and the model's existence and uniqueness are established by considering fixed point theory results. Sensitivity analysis is conducted on the model to identify the parameter with the highest sensitivity. For numerical treatment and simulations, we apply the Haar wavelet collocation method. Parameter estimation for the recorded COVID-19 cases in India from 13 July 2021 to 25 August 2021 has been presented.

Understanding Covid-19 Situation Using Mathematical Modeling

In light of the absence of an effective vaccine or specific antiviral treatments, mathematical modeling assumes a crucial role in enhancing our comprehension of disease dynamics and in devising strategies to manage the rapid spread of infectious diseases. Particularly during this period, forecasting holds paramount significance for healthcare planning and for effectively addressing the COVID-19 pandemic. To elucidate the dynamics of the COVID-19 outbreak, this study introduces an extended SEIR compartment model, refined with the inclusion of contact tracing and hospitalization strategies. The model is calibrated employing daily COVID-19 data encompassing various Indian regions, including Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal, and the entirety of India. Employing the least squares method, we estimate sensitive parameters after conducting a sensitivity analysis, which we approach using partial rank correlation coefficient techniques. Our exploration focuses on the relative significance of system parameters, with a dedicated sensitivity analysis centered on the reproductive number R₀. To assess the model's resilience across parameter variations, we compute R₀ sensitivity indices. Our findings underscore the effectiveness of strategies that involve reducing disease transmission coefficients (s) and clinical outbreak rates (qa) in controlling COVID-19 outbreaks. Our study generates short-term predictions for daily and cumulative confirmed COVID-19 cases across the five Indian provinces. These projections reveal distinct trends, with certain states demonstrating steady exponential growth, while others exhibit a decline in daily new cases. Examining the long-term perspective, our model predicts oscillatory dynamics for COVID-19 cases in India, suggesting the potential for the disease to follow a seasonal pattern. Consequently, our simulation points towards a power law trend in coronavirus cases in India by the close of September 2020, further contributing to our understanding of the disease's progression.

Long COVID Among Moderate to Severe COVID-19 Cases in India During Second Wave of COVID-19.

Long COVID Among Moderate to Severe COVID-19 Cases in India During Second Wave of COVID-19.

Automatic COVID-19 Diagnosis System Based on Deep Convolutional Neural Networks

A public health emergency threat is happening due to novel coronavirus 2019 (nCoV-2019) throughout the world. nCoV-2019 is also named Severe Acute Respiratory Syndrome-CoronaVirus-2 (SARS-CoV-2). COVID-19 is the disease caused by this virus. The virus originates in bats and is transmitted to humans by some unidentified intermediate animals. This virus started around December 2019 at Wuhan of China. After that, it turned into a pandemic. Even though there is no efficient vaccination, the entire world fights against the COVID-19. This article presents an overview of the scenario of the world as well as India. Some of the leading countries in the world are also affected by this virus badly. Even India is the 2nd highest population, is taking necessary precautions to protect it. With the Government of India's decisions, along with effective social distancing and hygienic measures, India is in a better position. But, in the future, COVID19 cases in India, still unpredictable. We designed an algorithm based on Convolutional Neural Network (CNN), which helps to classify COVID19+ and COVID19- persons using people's chest X-ray images automatically generated within the shortest time. The proposed method discovered that employing CT scan medical images produced more accurate results than X-ray images.

Study of Fractional Order SEIR Epidemic Model and Effect of Vaccination on the Spread of COVID-19.

In this manuscript, a fractional order SEIR model with vaccination has been proposed. The positivity and boundedness of the solutions have been verified. The stability analysis of the model shows that the system is locally as well as globally asymptotically stable at disease-free equilibrium point {E}_{0} when {mathfrak{R}}_{0}< 1 and at epidemic equilibrium {E}_{1} when {mathfrak{R}}_{0}> 1. It has been found that introduction of the vaccination parameter eta reduces the reproduction number {mathfrak{R}}_{0}. The parameters are identified using real-time data from COVID-19 cases in India. To numerically solve the SEIR model with vaccination, the Adam-Bashforth-Moulton technique is used. We employed MATLAB Software (Version 2018a) for graphical presentations and numerical simulations.. It has been observed that the SEIR model with fractional order derivatives of the dynamical variables is much more effective in studying the effect of vaccination than the integral model.

Evaluation and Prediction of End of Second-wave and Starting of Thirdwave Covid-19 Cases in India

Background: The second wave of coronavirus has appeared to be an extensive uphill of the number of daily new confirmed cases, recovered cases, and deaths than the first wave in India and the whole world. In India, the second wave of COVID-19 is much dangerous than the first wave that hit on 14th April 2020. The maximum number of new cases was 406901 recorded on May 7, 5.3 times more than the first wave peak. Many researchers worldwide are using machine learning prediction models to forecast the upcoming trends of this pandemic. Method: This study used an Autoregressive Integrated Moving Average (ARIMA) model to pre-dict the daily new confirmed cases, daily new deaths, and daily new recoveries between and after the second wave of COVID-19 in India. The dataset was collected from March 14, 2020- July 7, 2021, using the ARIMA model to predict corona cases for the next 60 days. Results: In the context of the current scenario in India, the second wave will score low new cases in mid-August 2021, and the third wave will hit the country in the middle of September 2021. Conclusion: The ARIMA model was chosen based on AIC (Akaike Information Criterion) values and acquired the maximum accuracy of 95%.

Coronavirus (COVID-19): ARIMA-based Time-series Analysis to Forecast near Future and the Effect of School Reopening in India

COVID-19, a novel coronavirus, is currently a major worldwide threat. It has infected more than a million people globally leading to hundred-thousands of deaths. In such grave circumstances, it is very important to predict future scenario to support prevention and recurrence of the disease, aid in healthcare service preparation and help in decision making process. Following that notion, a model has been developed for forecasting future COVID-19 cases in India. The time series analysis indicates that the cases will keep on increasing in India in the coming month as the peak has not been attained until now. A statistical analysis based on the effect of reopening of schools has also been performed. It is revealed that there will be a minor increase in the active cases when pre-/primary schools are opened. The present prediction models will assist the government and medical personnel in gaining insight and planning for forthcoming conditions.

Knowledge, Preventive Behaviour and Risk Perception about COVID-19 in Health Care Professional Students

Background: With 3.95 lakhs of active COVID-19 cases in India and Tamilnadu being the second-largest hub of COVID-19, the health and social impact on the public, especially the health care warriors would be considerable. Objective: To evaluate and compare the levels of knowledge, preventive behaviour and risk perception of South Indian Health Care Professional(HCP) Students regarding COVID-19.Methods: An institution-based cross-sectional questionnaire survey was conducted in a tertiary care centre and teaching hospital in May-June 2020 amongst 873 students pursuing various HCP courses. The questionnaire comprised four sections - demographic details, COVID–19 related knowledge, preventive behaviour and risk perception. Results: Females volunteered to complete the survey (n= 623; 71.4%) more than males (n= 250; 28.6%). Most participants had received awareness about COVID-19 (n=860; 98.5%) from many resources of information. While females (97.50 ± 8.94) had significantly higher scores on items for preventive behaviour than males (94.7±15.55; p=0.006), the scores were similar for knowledge and risk perception. There was a statistically significant difference in risk perception among various courses with students pursuing Pharmacy having higher risk perception. Items regarding the use of masks in general and hospital setups and availability of antivirals for COVID-19 received many incorrect responses. Conclusions: HCP students presented with high levels of COVID-19 related knowledge and preventive behaviour, but moderate risk perception. Continuing education programs and preventive behavioural training are the need of the hour to strengthen the knowledge and alleviate the anxiety of HCP students towards the pandemic.

A Statistical Non-Parametric data analysis for COVID-19 incidence data

Background:The impact of COVID-19 on the Global scale is tremendously drastic. There are several types of research going on across the world simultaneously to understand and overcome this dire pandemic outbreak. This paper is purely a statistical study on a distinct set of datasets regarding COVID-19 in India. The motivation of this study is to provide an insight into the rapid growth of confirmed COVID-19 cases in India. Methods:The rapid growth of COVID-19 cases in India started in March 2020. The main objective of this paper is to provide a solid statistical model for the policymaker to handle this kind of pandemic situation in the near future with nonlinear data. In this paper, the data was got from 1st April to 29th November 2020. To come up with a solid statistical model, various nonlinear data such as confirmed COVID-19 cases, maximum temperature, minimum temperature, the total population (state-wise), the total area in km2 (state-wise), and the total rural and urban population count (state-wise) have been analyzed. In this paper, six different Generalized Additive Models (GAM) was identified after a thorough analysis of other researchers’ (Xie and Zhu, 2020; Prata et al., 2020) findings. Results:In all perspectives, the results were identified and analyzed. The GAM model regarding total COVID-19 confirmed cases, total population, and the total rural population provides the best average fit of R2 value of 0.934. As the population value is quite high, the author has concise it using logarithm to provide the best p-value of 0.000542 and 0.001407 for a relation between the total number of COVID-19 cases regarding the total population and total rural population respectively.

Subcutaneous emphysema and pneumomediastinum in COVID -19 patients: ICU course of four cases and review of literature

Objective: Since April 2021, there has been significant increase in number of COVID-19 cases in India. As the caseload increased, so did the complications like pneumomediastinum (PM) and subcutaneous emphysema (SE). Case Presentation: We present clinical course of 4 patients as case series of COVID-19 pneumonia who developed PM and SE during their management. Two cases with the need of non-invasive ventilation (NIV) recovered and two cases that needed invasive mechanical ventilation (IMV) ultimately expired. Conclusion: This case series highlights the importance of positive pressure ventilation via NIV and IMV as a predisposing factor for PM and SE as well as the need of strict vigilance by clinicians managing COVID-19 patients in IMV.

Does IL-6 level help in assessment of severity in COVID-19 Pneumonia, and predicting radiological outcome? Tertiary care center experience of 1000 COVID-19 cases in India

Introduction: COVID-19 pneumonia is heterogeneous disease with variable effect on lung parenchyma, airways and vasculature leading to long term effects on lung functions. Materials and methods: Multicentric, prospective, observational and interventional study conducted during July 2020 to May 2021, in MIMSR Medical College and Venkatesh Hospital Latur India, included 1000 COVID-19 cases confirmed with RT PCR. All cases were assessed with lung involvement documented and categorized on HRCT thorax, oxygen saturation, inflammatory marker as IL-6 at entry point and follow up. Age, gender, Comorbidity and use BIPAP/NIV and outcome as with or without lung fibrosis as per CT severity were key observations. CT severity scoring done as per universally accepted standard scoring tool as score &lt;7 as mild, 7-14 as moderate and score &gt;15 as severe affection of lung. Statistical analysis is done by using Chi square test. Observations and analysis: In study of 1000 covid-19 pneumonia cases, age (&lt;50 and &gt;50 years) and gender (male versus female) has significant association with IL-6 in predicting severity of covid 19 pneumonia [p&lt;0.00001] &amp; [p&lt;0.010] respectively. CT severity score at entry point with IL-6 level has significant correlation in severity score &lt;8, 8- 15 and &gt;15 documented normal and abnormal IL-6 level as in 190/110, 90/210 and 40/360 respectively. [p&lt;0.00001] IL6 level has significant association with duration of illness i.e., Doi &lt;7 days, 8-15 days and &gt;15 days of onset of symptoms documented normal and abnormal IL-6 levels in 30/310, 160/300 and 130/70 cases respectively. [p&lt;0.00001] Comorbidity as diabetes mellitus, hypertension, COPD, IHD &amp; obesity has significant association in covid-19 cases with normal and abnormal IL-6 level respectively. [p&lt;0.00001] IL-6 level has significant association with oxygen saturation in covid1-19 pneumonia cases; cases with oxygen saturation &gt;90%, 75-90%, and &lt;75% observed as normal and abnormal IL-6 level in 110/100, 150/340 and 60/240 cases respectively [p&lt;0.00001] BIPAP/NIV requirement during course of covid-19 pneumonia in critical care setting has significant association with IL-6 level; cases received BIPAP/NIV during hospitalization were documented normal and abnormal IL-6 level in 155/445, 165/235 cases respectively [p&lt;0.00001] Timing of BIPAP/NIV requirement during course of covid-19 pneumonia in critical care setting has significant association with IL-6 level; cases received BIPAP/NIV at entry point &lt;1 day, 3-7 days and after 7 days of hospitalization were documented significance in four-fold raised IL-6 level in 110/70, 150/160 and 30/80 cases respectively [p&lt;0.00001] Follow-up IL-6 titer during hospitalization as compared to entry point abnormal IL-6 has significant association in postcovid lung fibrosis [p&lt;0.00001] Follow-up IL-6 titer during hospitalization as compared to entry point normal IL-6 has significant association in post-covid lung fibrosis [p&lt;0.00001] Follow-up IL-6 titer during hospitalization as compared to entry point abnormal IL-6 has significant association in predicting cytokine storm irrespective normal or abnormal of IL6 at entry point [p&lt;0.0001] Conclusion: IL-6 is sensitive, reliable, cost effective, and now universally acceptable inflammatory marker in COVID-19 pandemic. IL-6 has very crucial role in covid-19 pneumonia in predicting severity of illness, progression of illness including ‘cytokine storm’ and assessing response to treatment during hospitalization. Follow up IL-6 titer during hospitalization and at discharge can be used as early predictor of post-covid lung fibrosis.

Forecasting and comparative analysis of Covid-19 cases in India and US.

The devastating waves of covid-19 have wreaked havoc on the world, particularly India and US. The article aims to predict the real-time forecasts of covid-19 confirm cases for India and US. To serve the purpose, ARIMA and NNAR based models have been used to the daily new covid-19 confirm cases. The proposed hybrid models are: (i) ARIMA-NNAR model, (ii) NNAR-ARIMA model, (iii) ARIMA-Wavelet ARIMA model, (iv) ARIMA-Wavelet ANN model, (v) NNAR-Wavelet ANN model, and (vi) NNAR-Wavelet ARIMA model. The models are performed to predict the next 45 days of daily new cases. These forecasts can help Govt. to predict the behavior of covid -19 and aware people about the upcoming third wave of covid-19. Our results suggest that hybrid models perform better than single models. We have also proved that our wavelet-based hybrid models can outdated the performance of previously defined hybrid models in terms of accuracy assessments (MAE and RMSE). We have also estimated the time-dependent reproduction number for India and US to observe the present situation.

Interconnectedness and Nonlinearity in Indian Energy Futures During the COVID-19 Pandemic

This study examines interconnectedness and nonlinearity between energy futures indices, the exchange rate, and COVID-19 cases in India. Using a dynamic connectedness approach, the study confirms that, on average, 39.71% of the shock to one index spills over to all the other indices, including the exchange rate, whereas, on average, 60.29% of the shock affects itself. Further, the study finds nonlinear causality from crude oil futures to the exchange rate.