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• 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]