Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed.Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion.The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease.In the model, the three parameters, i.e.protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease.Raising people's awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. © 2023 Northeastern University.All rights reserved.

Correlation and Causation in the Perspective of COVID-19: An Empirical Study on Canonical Correlation Analysis

COVID-19 has an immense effect on the Globe, crossing 53,86,95,729 affected in more than 220 nations, with 63,18,093 individuals deceased. Various countries released COVID-19 protocols to enclose its spread to control the pandemic. This research article illustrates the Effect of COVID-19 on aged people (age>50), diabetes individuals, and individuals with smoking habits concerning the cause of death. An attempt has been made to identify the predominant variables for the cause of death due to COVID-19. IBM SPSS statistical tool enabled by Canonical Correlation Analysis (CCA) is used for simulation. Data were gathered from the Kaggle, an open repository for 2020. Based on the results obtained, predictions regarding the Cause and Effect of COVID-19 are discussed. © 2022 IEEE.

A Triangle Framework among Subgraph Isomorphism, Pharmacophore and Structure-function Relationship

Coronavirus disease 2019 (COVID-19) has gained utmost attention in the current time from academic research and industrial practices because it continues to rage in many countries. Pharmacophore models exploit molecule topological similarity as well as functional compound similarity so that they can be reliable via the application of the concept of bioisosterism. In this work, we analyze the targets for coronavirus protein and the structure of RNA virus variation, thereby complete the safety and pharmacodynamic action evaluation of small-molecule anti-coronavirus oral drugs. Common pharmacophore identifications could be converted into subgraph querying problems, due to chemical structures can also be converted to graphs, which is a knotty problem pressing for a solution. We adopt simplified representation pharmacophore graphs by reducing complete molecular structures to s to detect isomorphic topological patterns and further to improve the substructure retrieval efficiency. Our threefold architecture subgraph isomorphism-based method retrieves query subgraphs over large graphs. First, by means of extracting a sequence of subgraphs to be matched and then comparing the number of vertex and edge between the potential isomorphic subgraphs and the query graph, we lower the computational scaling markedly. Afterwards, the directed vertex and edge matrix recording vertex and edge positional relation, directional relation and distance relation has been created. Then, on the basis of permutation theorem, we calculate the row sum of vertex and edge adjacency matrix of query graph and potential sample. Finally, according to equinumerosity theorem, we check the eigenvalues of the vertex and edge adjacency matrices of the two graphs are equinumerous. The topological distance could be calculated based on the graph isomorphism and the subgraph isomorphism can be implemented after the combination of the subgraph. The proposed quantitative structure-function relationships (QSFR) approach can be effectively applied for pharmacophoric patterns identification. The framework of new drug development for covid-19 has been established based on this triangle. © 2022 ACM.

The analysis of cross-correlation between Istanbul Stock Exchange and major stock markets and indices: An empirical analysis using Random Matrix Theory

This study attempts to investigate the cross-correlation between stocks listed under the XU100 index of Borsa Istanbul with several ratios and indices of the stock markets worldwide by using the Random Matrix Theory approach through a correlation matrix. In addition, Eigenvector Analysis, Network Analysis, Dimension Reduction will be carried out to investigate cross-correlation between markets. It was found that XU100, which is an index that includes 100 stocks highest in volume, has a distinguishing behavior compared to other indices and rates in terms of eigenvalue and related eigenvector structures. Furthermore, mean-value portfolio analysis showed that the empirical correlation matrix underestimates the portfolio risks than the correlation matrix obtained by filtering the noise. Coronavirus pandemic also affected Borsa Istanbul by breaking periodic behavior of volatility and correlation cycle. © 2022 John Wiley & Sons, Ltd.

Theoretical Analysis of the SIRVVD Model for Insights into the Target Rate of COVID-19/SARS-CoV-2 Vaccination in Japan

The effectiveness of the first dose of vaccination for COVID-19 is different from that of the second dose;therefore, in several studies, various mathematical models that can represent the states of the first and second vaccination doses have been developed. Using the results of these studies and considering the effects of the first and second vaccination doses, we can simulate the spread of infectious diseases. The susceptible-infected-recovered-vaccination1-vaccination2-death (SIRVVD) model is one of the proposed mathematical models;however, it has not been sufficiently theoretically analyzed. Therefore, we obtained an analytical expression for the number of infected persons by considering the numbers of susceptible and vaccinated persons as parameters. We used the solution to determine the target vaccination rate for decreasing the infection numbers of the COVID-19 Delta variant (B.1.617) in Japan. Furthermore, we investigated the target vaccination rates for cases with strong or weak variants by comparing with the COVID-19 Delta variant (B.1.617). This study contributes to the mathematical development of the SIRVVD model and provides insights into the target vaccination rate for decreasing the number of infections. Author

Reconstruction of Time-varying Graph Signals via Sobolev Smoothness

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption. In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples. We explore some theoretical aspects of the convergence rate of our Time-varying Graph signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying the condition number of the Hessian associated with our optimization problem. Our algorithm has the advantage of converging faster than other methods that are based on Laplacian operators without requiring expensive eigenvalue decomposition or matrix inversions. The proposed GraphTRSS is evaluated on several datasets including two COVID-19 datasets and it has outperformed many existing state-of-the-art methods for time-varying graph signal reconstruction. GraphTRSS has also shown excellent performance on two environmental datasets for the recovery of particulate matter and sea surface temperature signals. IEEE

Deep Covariance Feature and CNN-based End-to-End Masked Face Recognition

With the emergence of the global epidemic of COVID-19, face recognition systems have achieved much attention as contactless identity verification methods. However, covering a considerable part of the face by the mask poses severe challenges for conventional face recognition systems. This paper proposes an automated Masked Face Recognition (MFR) system based on the combination of a mask occlusion discarding technique and a deep-learning model. Initially, a pre-processing step is carried out in which the images pass three filters. Then, a Convolutional Neural Network (CNN) model is proposed to extract the features from unoccluded regions of the faces (i.e., eyes and forehead). These feature maps are employed to obtain covariance-based features. Two extra layers, i.e., Bitmap and Eigenvalue, are designed to reduce the dimension and concatenate these covariance feature matrices. The deep covariance features are quantized to codebooks combined based on Bag-of-Features (BoF) paradigm. Finally, a global histogram is created based on these codebooks and utilized for training an SVM classifier. The proposed method is trained and evaluated on Real-World-Masked-Face-Recognition-Dataset (RMFRD) and Simulated-Masked-Face-Recognition-Dataset (SMFRD) achieves an accuracy of 95.07% and 92.32 %, respectively, showing its competitive performance compared to the state-of-the-art. Experimental results prove that our system has high robustness against noisy data and illumination variations. © 2021 IEEE.