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The COVID‐19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID‐19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID‐19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID‐19 epidemic in India. A Boltzmann Function‐based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN‐BP models. The COVID‐19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN‐BP models, and the latter was used to test them. The information examination uncovers critical day‐by‐day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN‐BP model that takes into consideration the preceding 14‐days outperforms the others based on the archived results. In forecasting the COVID‐19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN‐BP model that considers the past 14‐days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID‐19 outbreak. This work has not just demonstrated the relevance of the ANN‐BP techniques for the expectation of the COVID‐19 outbreak yet additionally showed that considering the incubation time of COVID‐19 in forecast models might produce more accurate assessments. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to indicate the current situation of the COVID-19 outbreak in India, the world's second-most populous country. India reported its first COVID-19 case in the southern province of Kerala in late January 2020. Since then, the outbreak has spread to other provinces and union territories, with the highest number of cases reported in Maharashtra province as of 24 May 2020. Design/methodology/approach: This paper presents the age- and gender-wise analysis of COVID-19 patients in Maharashtra province and the whole nation. Findings: It shows that 75% of them belong to the age group of 21–60 years and the age above 50 years has a very high fatality rate of 14.52% in Maharashtra. This analysis also shows that 76% of COVID-19 cases are in males in the nation. The data indicates that in the nation, 75.3% of the deaths are in the age group over 60 years and 86% of cases of death had diabetes-related comorbidity, hypertension, chronic kidney problems and heart problems. In India, the case-fatality rate, which estimates the proportion of deaths among identified confirmed cases, is 2.93% as of 24 May, which is significantly less than that many countries. Originality/value: To the best of the author's knowledge, this is the first attempt to indicate the current situation of the COVID-19 outbreak in India based on age and gender. [ABSTRACT FROM AUTHOR]

Mucormycosis is caused by fungi belonging to the order Mucorales. The term "Black Fungus" has been widely applied to human pathogenic Mucorales in India. They mainly infect the sinuses and brain, lungs, stomach and intestines, and skin. While this has been considered a rare disease, thousands of cases have been reported during the second wave of COVID-19 in India, between the months of April and June 2021. Hitherto, more than 45,374 cases and over 4300 deaths have been reported among COVID-19 patients across India from April 2021 to July 21, 2021. Though the mortality rate is estimated to be 50%, it could be above 90% if left untreated. In India, Rhizopus arrhizus has been related to be the most common species to cause human mucormycosis, followed by Apophysomyces variabilis, Rhizopus microsporus, and R. homothallicus. Accurate sample identification of human pathogenic Mucorales species is challenging especially due to the frequent lack of diagnostic morphological features. Traditionally, the culture-based approach has been extensively used to isolate and characterize human pathogenic Mucorales. However, this may not be an appropriate approach to objectively isolate and characterize all species, as the germination and growth of fungal spores are highly dependent on culture media and environmental conditions. Therefore, a robust approach to the accurate and rapid identification of human pathogenic Mucorales species is a prerequisite. The metagenomic approach comprehensively sequences and analyzes all genetic material in a complex biological sample and, consequently, this could be an appropriate approach to objectively characterize human pathogenic Mucorales taxa without the need for in vitro culture. The precise identification of the species will not only be useful for the correct diagnosis of this disease, but also for the development of antifungal drugs specific for each species. Accurate and rapid species identification is desperately needed to save lives in the mucormycosis outbreak among COVID-19 patients in India and neighboring countries. [ABSTRACT FROM AUTHOR]

• COVID-19 exerted extra pressure on the limited capacity of HCWTFs. • Setting up temporary HCWTFs to handle explosive growth of HCWs during pandemics. • Study identifies criteria to evaluate various temporary HCWTF location alternatives. • Proposed hybrid approach based on G-AHP and G-OCRA to sequence the location alternatives. • Optimal location for setting up temporary HCWTF is selcetd in Sundargarh District, Odisha, India. The COVID-19 outbreak has not only put the community health at stake but, also the environmental health. Usually, the healthcare wastes (HCWs) are composed of 15-20% of the infectious wastes and the rest of the non-infectious wastes. But, during any communicable health outbreak like COVID-19, the whole HCWs coming from the infected people become contagious. During the COVID-19 outbreak, the infectious waste is not only limited to the hospitals' premises, but also comes from the households, where COVID19 infected people are under home quarantine. Hence, keeping in mind the explosive growth in generation rates of infectious HCWs, the present study targets to expand the treatment and disposal capacity by installing temporary healthcare waste treatment facilities (HCWTFs). The study identifies ten criteria from the literature review and in consultation with the field experts, to evaluate the potential candidates for setting up temporary HCWTF during the health outbreaks. The study proposes a hybrid methodology based on grey analytical hierarchy process (G-AHP) and grey operational competitiveness rating analysis (grey-OCRA) for prioritizing the evaluation criteria and selecting the optimal temporary HCWTF location by considering the experts' inputs, respectively. The stakeholders consider the 'proximity to the inhabitation', 'infrastructure availability', and 'transportation distance' are the most important criteria for selecting the temporary HCWTF location. The proposed methodology is applied to select the temporary HCWTF location in Sundargarh District, Odisha, India. The study identifies the four locations by using geographical information system (GIS) tools and sequences them as per the preferences given by the stakeholders on various identified criteria. The study may be useful for the administration to set up the temporary facilities to quickly dispose of the extra HCWs during the pandemics. However, the future studies can be targeted to coordinate the collection, storage and transportation activities with the temporary HCWTFs. [Display omitted] [ABSTRACT FROM AUTHOR]

This article tests five major economies of the world, United Kingdom, Japan, Brazil, Chin and lastly, India, for the changes in the monetary policy decisions that have been implemented following the Covid-19 outbreak. The assessment was undertaken in the form of an event study analysis, further substantiated with a regression analysis conducted for exploring the significance of CPI and real GDP in predicting the policy interest rates in the economy. The results of the event study analysis presented that the abnormal changes in the interest rates were statistically significant in the case of the United Kingdom, Brazil, and China, while the abnormal changes were found to be statistically insignificant in the case of India and Japan., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Li, Sun and Chen.)

The coronavirus disease-2019 (COVID-19) pandemic has put healthcare services all over the world into a challenging situation. The contagious nature of the disease and the respiratory failure necessitating ventilatory care of these patients have put extra burden on intensive care unit (ICU) services. India has been no exception; by March 2020, the number of COVID-19 patients started increasing in India. This article describes the measures taken and challenges faced in creating ample ICU bed capacity to cater to the anticipated load of patients in the state of Delhi, India, as a result of the COVID-19 pandemic. The main challenges faced, among others, were estimating the number of ICU beds to be created; deciding on dedicated hospitals to treat COVID-19 patients; procuring ventilators, personal protective equipment, and other related material; mobilizing human resources and providing their training; and providing isolated in-house accommodations to the staff on duty. The authors acknowledge and agree that the methodology proposed in this article is but one way of approaching this difficult scenario and that there could be other, perhaps better, methods of dealing with such a problem., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Most of the countries are affected with the pandemic outbreak of the coronavirus infection. Understanding the severity and distribution in various regions will help in planning the controlling measures., Objectives: The objective was to assess the distribution and growth rate of COVID-19 infection in Tamil Nadu, India., Methods: The data on the number of infections of COVID-19 have been obtained from the media reports released by the Government of Tamil Nadu. The data contain information on the incidence of the disease for the first 41 days of the outbreak started on March 7, 2020. Log-linear model has been used to estimate the progression of the COVID-19 infection in Tamil Nadu. Separate models were employed to model the growth rate and decay rate of the disease. Spatial Poisson regression was used to identify the high-risk areas in the state., Results: : The models estimated the doubling time for the number of cases in growth phase as 3.96 (95% confidence interval [CI]: 2.70, 9.42) days and halving time in the decay phase as 12.08 (95% CI: 6.79, 54.78) days. The estimated median reproduction numbers were 1.88 (min = 1.09, max = 2.51) and 0.76 (min = 0.56, max = 0.99) in the growth and decay phases, respectively. The spatial Poisson regression identified 11 districts as high risk., Conclusion: The results indicate that the outbreak is showing decay in the number of infections of the disease which highlights the effectiveness of controlling measures., Competing Interests: None