• COVID-19
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PurposeThis “impact pathways” paper argues that operations and supply chain management (OSCM) could help address the worsening drug shortage problem in high-income countries. This significant societal problem poses difficult challenges to stakeholders given the complex and dynamic nature of drug supply chains. OSCM scholars are well positioned to provide answers, introducing new research directions for OSCM in the process.Design/methodology/approachTo substantiate this, the authors carried out a review of stakeholder reports from six European countries and the academic literature.FindingsThere is little academic research and no fundamental agreement among stakeholders about causes of shortages. Stakeholders have suggested many government measures, but little evidence exists on their comparative cost-effectiveness.Originality/valueThe authors discuss three pathways of impactful research on drug shortages to which OSCM could contribute: (1) Developing an evidence-based system view of drug shortages; (2) Studying the comparative cost-effectiveness of key government interventions; (3) Bringing supply chain risk management into the government and economics perspectives and vice versa. Our study provides a baseline for future COVID-19-related research on this topic.

The new corona virus 2019 (COVID-19) has become the most pressing issue facing mankind. Like a wildfire burning through the world, the COVID-19 disease has changed the global landscape in only one year. In this mini-review, a novel image classifier based on Kernel Extreme Learning Machine (KELM) and Kernel Principal Component Analysis (KPCA) is presented. The proposed algorithm called KELM-KPCA, aims to detect COVID-19 disease in chest radiographs, using a constrained dataset.

We propose two stochastic models for the Coronavirus pandemic. The statistical properties of the models, in particular the correlation functions and the probability density functions, were duly computed. Our models take into account the adoption of lockdown measures as well as the crucial role of hospitals and health care institutes. To accomplish this work we adopt a kinetic-type reaction approach where the modelling of the lockdown measures is obtained by introducing a new mathematical basis and the intensity of the stochastic noise is derived by statistical mechanics. We analysed two scenarios: the stochastic SIS-model (Susceptible ⇒ Infectious ⇒ Susceptible) and the stochastic SIS-model integrated with the action of the hospitals; both models take into account the lockdown measures. We show that, for the case of the stochastic SIS-model, once the lockdown measures are removed, the Coronavirus infection will start growing again. However, the combined contributions of lockdown measures with the action of hospitals and health institutes is able to contain and even to dampen the spread of the SARS-CoV-2 epidemic. This result may be used during a period of time when the massive distribution of vaccines in a given population is not yet feasible. We analysed data for USA and France. In the case of USA, we analysed the following situations: USA is subjected to the first wave of infection by Coronavirus and USA is in the second wave of SARS-CoV-2 infection. The agreement between theoretical predictions and real data confirms the validity of our approach. info:eu-repo/semantics/published SCOPUS: ar.j

While ground truth depth data remains hard to obtain, self-supervised monocular depth estimation methods enjoy growing attention. Much research in this area aims at improving loss functions or network architectures. Most works, however, do not leverage self-supervision to its full potential. They stick to the standard closed world train-test pipeline, assuming the network parameters to be fixed after the training is finished. Such an assumption does not allow to adapt to new scenes, whereas with self-supervision this becomes possible without extra annotations.In this paper, we propose a novel self-supervised Continuous Monocular Depth Adaptation method (CoMoDA), which adapts the pretrained model on a test video on the fly. As opposed to existing test-time refinement methods that use isolated frame triplets, we opt for continuous adaptation, making use of the previous experience from the same scene. We additionally augment the proposed procedure with the experience from the distant past, preventing the model from overfitting and thus forgetting already learnt information.We demonstrate that our method can be used for both intra- and cross-dataset adaptation. By adapting the model from train to test set of the Eigen split of KITTI, we achieve state-of-the-art depth estimation performance and surpass all existing methods using standard architectures. We also show that our method runs 15 times faster than existing test-time refinement methods. The code is available at https://github.com/Yevkuzn/CoMoDA.

Due to the strict personnel control measures in COVID-19 epidemic, the control system cannot be maintained and managed manually This puts forward higher requirements for the accuracy of its fault-tolerant performance The control system plays an increasingly important role in the rapid development of industrial production When the sensor in the system fails, the system will become unstable Therefore, it is necessary to accurately and quickly diagnose the faults of the system sensors and maintain the system in time This paper takes the control system as the object to carry out the fault diagnosis and fault-tolerant control research of its sensors A network model of wavelet neural network is proposed, and an improved genetic algorithm is used to optimize the weights and thresholds of the neural network model to avoid the deficiencies of traditional neural network algorithms For the depth sensor of a certain system, an online fault diagnosis scheme based on RBF (Radial Basis Function) neural network and genetic algorithm optimized neural network was designed The disturbance fault, 'stuck' fault, drift fault and oscillation fault of the depth sensor are simulated Simulation experiments show that both online fault diagnosis schemes can accurately identify sensor faults and the genetic algorithm optimized neural network is superior to RBF neural network in both recognition accuracy and training time under the influence of COVID-19 © 2020 - IOS Press and the authors All rights reserved

The COVID-19 epidemic broke out at the end of 2019 and developed into a global infectious disease in early 2020. In order to understand the spreading trend of the epidemic, we propose an enhanced epidemiology predictive model—eSEIR model by improving the well-mixed SEIR model on the infectious disease dynamics. The eSEIR model incorporates an optimization method to calculate β and γ parameters. Our proposed model is verified using the epidemic data in Italy and China with reduced RMSE (root mean square error) of the predicted curves, and is used to predict the potential epidemic progress in the United States.

The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs, it would be useful to determine which features presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes. We developed a model that employed supervised machine learning algorithms to identify the presentation features predicting COVID-19 disease diagnoses with high accuracy. Features examined included details of the individuals concerned, e.g., age, gender, observation of fever, history of travel, and clinical details such as the severity of cough and incidence of lung infection. We implemented and applied several machine learning algorithms to our collected data and found that the XGBoost algorithm performed with the highest accuracy (>85%) to predict and select features that correctly indicate COVID-19 status for all age groups. Statistical analyses revealed that the most frequent and significant predictive symptoms are fever (41.1%), cough (30.3%), lung infection (13.1%) and runny nose (8.43%). While 54.4% of people examined did not develop any symptoms that could be used for diagnosis, our work indicates that for the remainder, our predictive model could significantly improve the prediction of COVID-19 status, including at early stages of infection. Highlights • Machine learning was used to develop models to predict COVID-19 positive patient. • Features were extracted from patient data using string matching algorithms. • Constructed a novel dataset from unstructured hospitalized patient information. • Used descriptive statistical analysis for frequency calculation of patient symptoms. • Identified significant symptoms of COVID-19 patients using five different ML models.

The 2019 coronavirus pandemic which started infecting the people of Wuhan, China during December 2019 has affected many countries worldwide within a span of 4–5 months. This has forced the countries to close their borders resulting in a global lockdown. The World Health Organization declared the disease as a pandemic during early March this year. As of 15th April 2020, nearly 3 months since the spread of the disease, no vaccine has been developed and preventive measures such as social distancing and countrywide lockdown seem to be the only way to prevent it from spreading further. The rising death toll indicates the need to carry out extensive research to aid medical practitioners as well as the governments worldwide to comprehend the rapid spread of the disease. While many research papers have been published explaining the origin and theoretical background of the disease, further research is needed to develop better prediction models. The data for the problem was generated from the sources available during the course of this study. This paper extensively analyzes the medical features of 269 patients using various Machine Learning techniques such as KNN, Random Forest, Ridge classifier, Decision Tree, Support Vector Classifier and Logistic Regression. The paper aims to predict the fatality status of an individual diagnosed with COVID-19 by assessing various factors including age, symptoms, etc. The experimental results from the research would help medical practitioners to identify the patients at higher risk and require extra medical attention, thereby helping the medical practitioners to prioritize them and increase their chances of survival.

Abstract Problem A worldwide challenge is to provide medical resources required for COVID-19 detection. They must be effective tools for fast detection and diagnose of the virus using a large number of tests; besides, they should be low-cost developments. While a chest X-ray scan is a powerful candidate tool, if several tests are carried out, the images produced by the devices must be interpreted accurately and rapidly. COVID-19 induces longitudinal pulmonary parenchymal ground-glass and consolidates pulmonary opacity, in some cases with rounded morphology and peripheral lung distribution, which is very difficult to predict in an early stage. Aim In this paper, we aim to develop a robust model to extract high-level features of COVID-19 from chest X-ray (CXR) images to help in rapid diagnosis. In specific, this paper proposes an optimization model for COVID-19 diagnosis based on adaptive Fuzzy C-means (AFCM) and improved Slime Mould Algorithm (SMA) based on Levy distribution, namely AFCM-LSMA. Methods The SMA optimizer is proposed to adapt weights in oscillation mode and to mimic the process of generating positive and negative feedback from the propagation wave to shape the optimum path for food connectivity. Levy motion is used as a permutation to perform a local search and to adapt SMA optimizer (LSMA) by generating several solutions that are apart from current candidates. Furthermore, it permits the optimizer to escape from local minima, examine large search areas and reach optimal solutions in fewer iterations with high convergence speed. The FCM algorithm is used to segment pulmonary regions from CXR images and is adapted to reduce time and amount of computations using histogram of the image intensities during the clustering process. Results The performance of the proposed AFCM-LSMA has been validated on CXR images and compared with different conventional machine learning and deep learning techniques, meta-heuristics methods, and different chaotic maps. The accuracies achieved by the proposed model are around (ACC = 0.96, RMSE = 0.23, Prec. = 0.98, F1_score = 0.98, MCC = 0.79, and Kappa = 0.79). Conclusion The experimental findings indicate that the proposed new method outperforms all other methods, which will be beneficial to the clinical practitioner for the early identification of infected COVID-19 patients.

The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug–virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug–virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug–virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug–virus network, which integrates the known drug–virus interactions, drug chemical structures, and virus genomic sequences. SCPMF projects the drug–virus interactions matrix into two latent feature matrices for the drugs and viruses, which reconstruct the drug–virus interactions matrix when multiplied together, and then introduces the weighted similarity interaction matrix as constraints for drugs and viruses. Benchmarking comparisons on two different datasets demonstrate that SCPMF has reliable prediction performance and outperforms several recent approaches. Moreover, SCPMF-predicted drug candidates of COVID-19 also confirm the accuracy and reliability of SCPMF. Highlights • We build the Human Drug Virus Database (HDVD). • SCPMF introduces similarity constraints into the probabilistic matrix factorization. • SCPMF offers a useful model to help effectively identify prospective drugs.