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The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies., (© 2024. The Author(s).)

Hotel industry is the one which has confronted the unprecedented effect of the coronavirus disease 2019 (COVID-19) pandemic to significant social and economic risks. The COVID-19 pandemic has challenged the tourism across the globe and impacted hospitality in hotel industry severely. This study aims to assess customer satisfaction by carrying sentiment analysis and topic modelling over customer reviews on the hospitality provided by hotels in different continents during January to September 2020, i.e. the COVID-19 pandemic. We formulate an improved new scale of metrics to categorize customer satisfaction assessed by sentiment analysis in an elaborate way. Topic modelling was deployed to understand various topics most often discussed by customers. We find that North America and Europe could perform up to customer expectation. In Asia, Sri Lanka did well, Indonesia could maintain its customer satisfaction, while India consistently improved the satisfaction level. We identified 12 most discussed topics, and main reasons of dissatisfaction appear in staff, service, room, cleanliness, slow booking, and pandemic response by hotel. Findings of this study will help senior managers of hotels of developed as well as developing countries in providing new and effective services that can satisfy customers and restore their confidence. [ABSTRACT FROM AUTHOR]

Many strains of corona virus such as alpha, beta, gamma, delta, and omicron are still prevalent in various parts of the world. The new type of corona virus is called a variant when it is caused by more than one genetic mutation from the previous type of corona virus. Various strains around the world have come so far. The cough may persist for more than an hour or three or four times in 24 hours and body heat is high. You may not be able to feel the smell or taste. Researchers say that some people may have symptoms similar to those of a severe cold. In this paper, a multi threshold model was proposed to find the initial infection detection of COVID disease. Based on the initial health symptoms these methods observe the inputs of the patients. Then the observed symptoms are compared with the existing database and identify the spreading of the disease. This report was directly monitored by the patient and doctor. This model was helpful to provide the periodical monitoring and perfect treatments to the infected patients. [ABSTRACT FROM AUTHOR]

• Fairness-aware Collaborative Filtering (FCF). • Adaptive and Concise Empirical Wavelet Transform (ACEWT). • Attention Induced Multi-head Convolutional Neural Network with MobileNetv1. • Classifies the COVID-19, like Normal, COVID-19, SARS, Pneumocystis. • The proposed C19D-AIMCNN-MNet-JSOA model experimentally authenticated. In this research, Attention Induced Multi-head Convolutional Neural Network Organization using MobileNetv1 Transfer Learning and COVID-19 Diagnosis using Jellyfish Search Optimization Process on Chest X-ray Images (C19D-AIMCNN-MNet-JSOA) is proposed. Initially, the input images are taken from chest X-ray dataset. Fairness-aware Collaborative Filtering (FCF) is utilized for eliminating the noise and also improves the X-ray image quality. Next, these pre-processed images are given to Adaptive and Concise Empirical Wavelet Transform (ACEWT) for extracting Grayscale statistic and Haralick Texture features. The extracted features are given into the Attention Induced Multi-head Convolutional Neural Network with MobileNetv1 (AIMCNN-MNet) which classifies the COVID-19, like Normal, COVID-19, SARS, Pneumocystis. In general, AIMCNN-MNet does not show any optimization adaption techniques to determine the ideal parameter to provide precise COVID-19 categorization. The proposed C19D-AIMCNN-MNet-JSOA model experimentally authenticated utilizing chest X-ray dataset in MATLAB and performance metrics including sensitivity, precision, F-Score, specificity, accuracy, Kappa, computation time, error rate used to examine the efficiency of proposed method. The performance of the C19D-AIMCNN-MNet-JSOA approach attains 25.99%, 20.34%, 30%, 19% and 20.35% high Precision, 25.43%, 29.53%, 22%, 28% and 25.31% lower computation Time and 15.249%, 25.491%, 10%, 31% and 13.98% higher RoC comparing with existing methods like novel hand-crafted fusion model founded on deep learning features COVID-19 diagnosis and organization using X-ray pictures of the chest (C19D-CNN-MLP), Multi-modal fusion of deep transfer learning founded COVID-19 diagnosis and organization utilizing chest x-ray images (C19D-MMF-DTL), Recognition and organization of lung diseases for pneumonia and Covid-19 utilizing machine along deep learning methods (C19D-RNN-LSTM). [ABSTRACT FROM AUTHOR]

• Best strategies for a large-scale COVID-19 vaccine distribution are developed. • Storage and transportation requirements, as well as costs are considered. • An equitable distribution is considered due to limited vaccine availability. • Best location and allocation decisions for cold storage facilities are provided. • The model is evaluated in a real-world case study. While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities. [ABSTRACT FROM AUTHOR]

• We investigate a location-allocation problem for convalescent plasma banks. • Optimal locations are determined, then collection facilities are allocated. • A MILP model considering transportation time and cost is solved via CPLEX. • Results are validated by a comparison study using NSGA-II and NSGA-III. • The model is implemented within the context of India and results are presented. With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers. [ABSTRACT FROM AUTHOR]