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Coronavirus Disease 2019 (COVID-19) continues to spread throughout the world, and it is necessary for us to implement effective methods to prevent and control the spread of the epidemic. In this paper, we propose a new model called Spatial–Temporal Attention Graph Convolutional Networks (STAGCN) that can analyze the long-term trend of the COVID-19 epidemic with high accuracy. The STAGCN employs a spatial graph attention network layer and a temporal gated attention convolutional network layer to capture the spatial and temporal features of infectious disease data, respectively. While the new model inherits the symmetric "space-time space" structure of Spatial–Temporal Graph Convolutional Networks (STGCN), it enhances its ability to identify infectious diseases using spatial–temporal correlation features by replacing the graph convolutional network layer with a graph attention network layer that can pay more attention to important features based on adaptively adjusted feature weights at different time points. The experimental results show that our model has the lowest error rate compared to other models. The paper also analyzes the prediction results of the model using interpretable analysis methods to provide a more reliable guide for the decision-making process during epidemic prevention and control. [ABSTRACT FROM AUTHOR]

The massive adaptation of reverse transcriptase-polymerase chain reaction (RT-PCR) has facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of individuals around the world. Besides RT-PCR, radiography imaging examinations yields valuable insight for detecting and diagnosing this infectious disease. Thus, this paper proposed a computer vision and artificial-intelligence-based hybrid approach aid in efficient detection and control of COVID-19 disease. The study utilized chest X-ray images to segregate COVID-19 positive cases among healthy individuals by exploiting several combinational structures of image filtering, feature-extraction techniques, and machine learning algorithms. It analyzed the effects of three noise removal filters and two feature-extraction techniques on performance of several machine learning and deep-learning-based classifiers. The proposed schemes first remove unnecessary noise using a conservative smoothing filter, Crimmins speckle removal, and Gaussian filter. It then employs linear discriminant analysis (LDA) as linear method and principal component analysis (PCA) as non-linear feature-extraction technique to extract highly discriminant feature sets. Finally, it uses these feature sets to train various classification models, including convolutional neural network (CNN), support vector machine (SVM), and logistic regression (LG). Evidently, the proposed conservative smoothing filter with single peak to maintain symmetry in horizontal and vertical directions for enhancement of image, along with LDA and SVM, secured an overall classification accuracy of 99.93%. Experimental results show that, besides achieving high accuracies, the incorporation of feature-extraction techniques significantly reduces the computational time of the proposed model. [ABSTRACT FROM AUTHOR]

This paper discusses and provides some analytical studies for a modified fractional-order SIRD mathematical model of the COVID-19 epidemic in the sense of the Caputo–Katugampola fractional derivative that allows treating of the biological models of infectious diseases and unifies the Hadamard and Caputo fractional derivatives into a single form. By considering the vaccine parameter of the suspected population, we compute and derive several stability results based on some symmetrical parameters that satisfy some conditions that prevent the pandemic. The paper also investigates the problem of the existence and uniqueness of solutions for the modified SIRD model. It does so by applying the properties of Schauder's and Banach's fixed point theorems. [ABSTRACT FROM AUTHOR]

The global stability analysis for the mathematical model of an infectious disease is discussed here. The endemic equilibrium is shown to be globally stable by using a modification of the Volterra–Lyapunov matrix method. The basis of the method is the combination of Lyapunov functions and the Volterra–Lyapunov matrices. By reducing the dimensions of the matrices and under some conditions, we can easily show the global stability of the endemic equilibrium. To prove the stability based on Volterra–Lyapunov matrices, we use matrices with the symmetry properties (symmetric positive definite). The results developed in this paper can be applied in more complex systems with nonlinear incidence rates. Numerical simulations are presented to illustrate the analytical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the efficient stochastic simulation of multiple scenarios of an infectious disease as it propagates through a population. In particular, we propose a simple "green" method to speed up the simulation of disease transmission as we vary the probability of infection of the disease from scenario to scenario. After running a baseline scenario, we incrementally increase the probability of infection, and use the common random numbers variance reduction technique to avoid re-simulating certain events in the new scenario that would not otherwise have changed from the previous scenario. A set of Monte Carlo experiments illustrates the effectiveness of the procedure. We also propose various extensions of the method, including its use to estimate the sensitivity of propagation characteristics in response to small changes in the infection probability. [ABSTRACT FROM AUTHOR]