The Application of PageRank model in urban epidemic prevention and control

The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.

The Relationship Between COVID-19 Cases and ArcGIS Technology to Present in Indonesia

Giving the COVID-19 vaccine has many benefits, including increasing immunity from exposure to COVID-19 and preventing new mutations from COVID-19. In addition, the COVID-19 vaccine that has been injected into the community has gone through a series of strict tests, so that it is guaranteed to be safe, quality and efficacious. The research aims to cluster the spread of the corona virus in DKI Jakarta province which is displayed on a visual map using ArcGIS Technology. Based on the data on the spread of the corona virus which has been grouped using K-means clustering, it is hoped that it can help make the right decisions in vaccination and the priority of COVID-19 assistance that is determined and directed based on information cluster, so this research is expected to help the government in tackling the COVID-19 pandemic in Indonesia, especially DKI Jakarta. In addition, this research also aims to see the correlation between the COVID-19 vaccine and the number of positive cases of Covid-19. © 2022 IEEE.

Multi-Joint Unit Network for COVID-19 Diagnosis using X-Ray and CT Images

The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.

A Comparative Study of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Covid Variants

The pandemic situation created by COVID'19 is ridiculous. It has made even the blood relations hide themselves from the infected person. The whole world was stunned by this situation. This is because of the uncertainty in the way in which this disease is spread. As an advancement of this disease, a few other variants like delta, omicron etc. also got spread. It is essential to find a solution to this situation. The variants Omicron and Delta are taken into consideration here. Though both the vibrant colours look alike, the symptoms and prevention methods changes for each of these vibrants. This work aims to make a study of the parameters responsible for these variants. As a result of this study, the parameters involved in the spread of these diseases are identified, and the prevention parameters are concluded. The major benefit of this comparatively study is to identify the parameters that are inconclusive, applying the concepts of fuzzy cognitive maps and neutrosophic cognitive maps is applied to bring out the result © 2023, Neutrosophic Sets and Systems.All Rights Reserved.

Robotic Assistance in Radiology: A Covid-19 Scenario

During the COVID-19 Pandemic, the need for rapid and reliable alternative COVID-19 screening methods have motivated the development of learning networks to screen COVID-19 patients based on chest radiography obtained from Chest X-ray (CXR) and Computed Tomography (CT) imaging. Although the effectiveness of developed models have been documented, their adoption in assisting radiologists suffers mainly due to the failure to implement or present any applicable framework. Therefore in this paper, a robotic framework is proposed to aid radiologists in COVID-19 patient screening. Specifically, Transfer learning is employed to first develop two well-known learning networks (GoogleNet and SqueezeNet) to classify positive and negative COVID-19 patients based on chest radiography obtained from Chest X-Ray (CXR) and CT imaging collected from three publicly available repositories. A test accuracy of 90.90%, sensitivity and specificity of 94.70% and 87.20% were obtained respectively for SqueezeNet and a test accuracy of 96.40%, sensitivity and specificity of 95.50% and 97.40% were obtained respectively for GoogleNet. Consequently, to demonstrate the clinical usability of the model, it is deployed on the Softbank NAO-V6 humanoid robot which is a social robot to serve as an assistive platform for radiologists. The strategy is an end-to-end explainable sorting of X-ray images, particularly for COVID-19 patients. Laboratory-based implementation of the overall framework demonstrates the effectiveness of the proposed platform in aiding radiologists in COVID-19 screening. Author

The Road Ahead (Editors)

This final chapter of the book "Frontiers of COVID-19: Scientific and Clinical Perspectives of the Novel SARS-CoV-2" takes the contents of the book and lesson learned of the clinical and epidemiological aspects of COVID-19 to the next level and provides guidelines and the road map into the future, beyond COVID-19. The aim is to present the most recent understanding of the fast-changing dynamics of COVID-19 and to help our clinicians and physicians better prepare for the road ahead. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A hybrid agent-based model integrated with a multi-stage learning-based fuzzy cognitive map

PurposeThis article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.Design/methodology/approachThe model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios.FindingsThe findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics.Originality/valueThe proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.

Optimal Path Control of a Patient for Rapid Infection Testing

In this paper, we propose a path control framework for guiding and simulating the patient's path of travel to speed up virus testing in pandemic situations, such as COVID-19. We use geographic information and hospital state information to construct graphs to yield optimal travel paths. Pathfinding algorithms A* and Navigation mesh, which have been widely used, are efficient when applied to control agents in a virtual environment. However, they are not suitable for real-time changing cases such as the COVID-19 environment because they guide only predetermined static routes. In order to receive a virus infection test quickly, there are many factors to consider, such as road traffic conditions, hospital size, number of patient movements, and patient processing time, in addition to guiding the shortest distance. In this paper, we propose a framework for digitally twinning various situations by modeling optimization functions considering various environmental factors in real-world urban maps to handle viral infection tests quickly and efficiently.

Livelihood Zonation Mapping: An Unplugged Sect of Regional Science

Amidst the global health emergency, when couples of academicians are devoted to pursuing their research linked with COVID-19, this present chapter is purposively concerned with the lesser-highlighted issue of customarily categorized livelihoods scenario, on a spatial basis, in one of the nations of Global South. The prime objective of the present section is to find out the fundamental fashion of regional deviation as well as the concentration of livelihood and future suggestions for suitable policy proposals. While this volume is systematically based on secondary datasets from recognized sources and the methods are being adopted after judiciary modification of established modus operandi like ‘crop combination', ‘location quotient', ‘crop diversification', and ‘GDP geographical area ratio'. On the other side, for the overall ‘livelihood zone map' (LZM), the standard ‘Z'-score method, and GIS mapping tool have been used. Although the regional data-oriented outcome is much voluminous, in a nutshell, it can be affirmed that in Goa, Delhi (NCR), West Bengal, and Manipur, the quality of livelihood condition is well, while among rest of the Indian states and union territories, the status is below the desired level. Conclusively, for a more precise and area-oriented suitable policy proposal, more research work has been needed for the novel development of livelihood conditions and the country's economic base. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

Data Visualization for Developing Effective Performance Dashboard with Power BI

Data visualization is a very important step in data analysis as it provides insight into the data in a more effective manner that is interesting, simple, and understandable to every-one without any language barrier. It can also represent a huge amount of data in a small space very easily. In the previous two years, the whole world has suffered from a very terrifying nightmare known as COVID-19. Known to be starting from the country of China, the pandemic affected not only the health and well-being of mankind, but also had serious impacts on the economies of various countries. Hence, a visualization of the data set of the pandemic might provide beneficial insights for finding a possible solution and can help in overcoming the impacts of the pandemic. Microsoft Power BI is a very famous tool for analyzing data. Power BI provides a different way to visualize the data. This paper has been analyzed the covid-19 data by using Power BI to understand the trends and patterns of the Pandemic. With the help of visualizing the data, it can be represented in stacked column charts, tables, and maps. These three ways are easy and simple to understand the patterns of the pandemic. It also helps to understand how covid impact the world. This research with power BI dashboard by using a dashboard feature that connects different pieces of visual graphs. © 2023 IEEE.

Design and Implementation of Epidemic Big Data Visualization Platform Based on Vue + ECharts

Data visualization technology makes massive data more intuitive and easy to analyze. Based on the epidemic data from the National Bureau of Statistics of China, with the help of ECharts chart, elementUI component library and Vue technology, the data are visualized by using visualization technology and map integration. Through node. JS, Express The framework and MySQL technology realize the annual data management, regional data management and user management of the epidemic situation, display the epidemic situation of each region from multiple perspectives, and provide users with a reliable and convenient understanding channel and data management platform. It provides convenience for people to understand the data of the new coronavirus epidemic, analyze the development trend of the epidemic and manage the big data of the epidemic. © 2023 IEEE.

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

Advances in digital neuroimaging technologies, i.e., MRI and CT scan technology, have radically changed illness diagnosis in the global healthcare system. Digital imaging technologies produce NIfTI images after scanning the patient's body. COVID-19 spared on a worldwide effort to detect the lung infection. CT scans have been performed on billions of COVID-19 patients in recent years, resulting in a massive amount of NIfTI images being produced and communicated over the internet for diagnosis. The dissemination of these medical photographs over the internet has resulted in a significant problem for the healthcare system to maintain its integrity, protect its intellectual property rights, and address other ethical considerations. Another significant issue is how radiologists recognize tempered medical images, sometimes leading to the wrong diagnosis. Thus, the healthcare system requires a robust and reliable watermarking method for these images. Several image watermarking approaches for .jpg, .dcm, .png, .bmp, and other image formats have been developed, but no substantial contribution to NIfTI images (.nii format) has been made. This research suggests a hybrid watermarking method for NIfTI images that employs Slantlet Transform (SLT), Lifting Wavelet Transform (LWT), and Arnold Cat Map. The suggested technique performed well against various attacks. Compared to earlier approaches, the results show that this method is more robust and invisible.

Focusing on Governance for a Real Client in a Content Strategy Course

This article describes a graduate seminar on Content Strategy taught in the Fall of 2020 during the height of the COVID pandemic. Students worked totally online with a real client to develop a content strategy plan. This class was noteworthy because, unlike most classes that end up designing a logo, identity package, and look-n-feel approach to content strategy, this course ended up focusing on the much-overlooked emphasis on governance in an already well-established content strategy plan. Students conducted a persona research study (using Redish's approach) and built a UX journey map (using Kalbach's approach). They conducted a content audit (using Halverson's approach) and then used the data to determine what problems in content development really needed to be solved. These analyses showed that the client's principal needs actually dealt with governance issues rather than logos, branding, and content, so students researched and recommended suitable governance systems (primarily following Welchman's approach). Finally, they produced templates, sample content, and a content development plan for PCLS based on the new governance model provided. [ FROM AUTHOR] Copyright of Journal of Technical Writing & Communication is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

SIRSi-vaccine dynamical model for the Covid-19 pandemic.

Covid-19, caused by severe acute respiratory syndrome coronavirus 2, broke out as a pandemic during the beginning of 2020. The rapid spread of the disease prompted an unprecedented global response involving academic institutions, regulatory agencies, and industries. Vaccination and nonpharmaceutical interventions including social distancing have proven to be the most effective strategies to combat the pandemic. In this context, it is crucial to understand the dynamic behavior of the Covid-19 spread together with possible vaccination strategies. In this study, a susceptible-infected-removed-sick model with vaccination (SIRSi-vaccine) was proposed, accounting for the unreported yet infectious. The model considered the possibility of temporary immunity following infection or vaccination. Both situations contribute toward the spread of diseases. The transcritical bifurcation diagram of alternating and mutually exclusive stabilities for both disease-free and endemic equilibria were determined in the parameter space of vaccination rate and isolation index. The existing equilibrium conditions for both points were determined in terms of the epidemiological parameters of the model. The bifurcation diagram allowed us to estimate the maximum number of confirmed cases expected for each set of parameters. The model was fitted with data from São Paulo, the state capital of SP, Brazil, which describes the number of confirmed infected cases and the isolation index for the considered data window. Furthermore, simulation results demonstrate the possibility of periodic undamped oscillatory behavior of the susceptible population and the number of confirmed cases forced by the periodic small-amplitude oscillations in the isolation index. The main contributions of the proposed model are as follows: A minimum effort was required when vaccination was combined with social isolation, while additionally ensuring the existence of equilibrium points. The model could provide valuable information for policymakers, helping define disease prevention mitigation strategies that combine vaccination and non-pharmaceutical interventions, such as social distancing and the use of masks. In addition, the SIRSi-vaccine model facilitated the qualitative assessment of information regarding the unreported infected yet infectious cases, while considering temporary immunity, vaccination, and social isolation index.

In vivo secondary structural analysis of Influenza A virus genomic RNA.

Influenza A virus (IAV) is a respiratory virus that causes epidemics and pandemics. Knowledge of IAV RNA secondary structure in vivo is crucial for a better understanding of virus biology. Moreover, it is a fundament for the development of new RNA-targeting antivirals. Chemical RNA mapping using selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) coupled with Mutational Profiling (MaP) allows for the thorough examination of secondary structures in low-abundance RNAs in their biological context. So far, the method has been used for analyzing the RNA secondary structures of several viruses including SARS-CoV-2 in virio and in cellulo. Here, we used SHAPE-MaP and dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) for genome-wide secondary structure analysis of viral RNA (vRNA) of the pandemic influenza A/California/04/2009 (H1N1) strain in both in virio and in cellulo environments. Experimental data allowed the prediction of the secondary structures of all eight vRNA segments in virio and, for the first time, the structures of vRNA5, 7, and 8 in cellulo. We conducted a comprehensive structural analysis of the proposed vRNA structures to reveal the motifs predicted with the highest accuracy. We also performed a base-pairs conservation analysis of the predicted vRNA structures and revealed many highly conserved vRNA motifs among the IAVs. The structural motifs presented herein are potential candidates for new IAV antiviral strategies.

Forward-backward and period doubling bifurcations in a discrete epidemic model with vaccination and limited medical resources.

A discrete epidemic model with vaccination and limited medical resources is proposed to understand its underlying dynamics. The model induces a nonsmooth two dimensional map that exhibits a surprising array of dynamical behavior including the phenomena of the forward-backward bifurcation and period doubling route to chaos with feasible parameters in an invariant region. We demonstrate, among other things, that the model generates the above described phenomena as the transmission rate or the basic reproduction number of the disease gradually increases provided that the immunization rate is low, the vaccine failure rate is high and the medical resources are limited. Finally, the numerical simulations are provided to illustrate our main results.

A survey of image encryption for healthcare applications.

Recently, medical image encryption has attracted many researchers because of security issues in the communication process. The recent COVID-19 has highlighted the fact that medical images are consistently created and disseminated online, leading to a need for protection from unauthorised utilisation. This paper intends to review the various medical image encryption approaches along with their merits and limitations. It includes a survey, a brief introduction, and the most utilised interesting applications of image encryption. Then, the contributions of reviewed approaches are summarised and compared regarding different technical perspectives. Lastly, we highlight the recent challenges along with several directions of potential research that could fill the gaps in these domains for researchers and developers.

[Geospatial models for SARS-CoV-2 outbreak control in Cartagena and Barranquilla, Colombia, 2020Modelos geoespaciais para controle de surtos de SARS-CoV-2 em Cartagena e Barranquilla, Colômbia, 2020]. / Modelos geoespaciales para control de brotes de SARS-CoV-2 en Cartagena y Barranquilla, Colombia, 2020.

Objective: Determine the temporal and spatial structure of the severe acute respiratory syndrome virus (SARS-CoV-2) that causes coronavirus disease (COVID-19), in the cities of Cartagena and Barranquilla, Colombia, in order to take necessary actions to support contact tracing. Methods: Cross-sectional ecological study with spatial analysis based on kernel densities of variables, including cases, mobile application alerts, population vulnerability, multidimensional poverty index; inverse distance weighted spatial interpolation of active cases; and, finally, the spatial superposition technique as a final result. The database of the National Institute of Health of the cities of Cartagena and Barranquilla and the Department of National Statistics was used. Results: The analysis identified an upward epidemiological trend in cases in the two cities, and determined the spatial direction of disease spread in neighborhoods, through spatial interpolation. Intervention areas were detected in 15 neighborhoods in Cartagena and 13 in Barranquilla, 50 meters around active cases with fewer than 21 days of evolution and by geographical risk layers, as a mechanism to stop the spread of COVID-19. Conclusions: Spatial analysis proved to be a useful complementary methodology for contact tracing, by determining temporal and spatial structure and providing necessary scientific evidence for the application of direct intervention measures, where necessary, to reduce the spread of SARS-CoV-2.

Objetivo: Determinar a estrutura temporal e espacial do vírus da síndrome respiratória aguda grave (SARS-CoV-2, na sigla em inglês), causador da doença pelo coronavírus de 2019 (COVID-19, na sigla em inglês), nas cidades de Cartagena e Barranquilla, visando a tomar ações necessárias que apoiem o rastreamento de contatos. Métodos: Estudo ecológico transversal que inclui análise espacial baseada em densidade de Kernel de variáveis como casos, alertas de um aplicativo móvel, vulnerabilidade populacional, índice de pobreza multidimensional, aplicação de interpolação espacial (IDW, na sigla em inglês) de casos ativos e, por último, aplicação da técnica de sobreposição espacial como resultado final. Foram utilizadas as bases de dados do Instituto Nacional de Saúde para as cidades de Cartagena e Barranquilla e do Departamento Nacional de Estatística. Resultados: A análise determinou o comportamento epidemiológico ascendente dos casos nas duas cidades e identificou a direção espacial de propagação da doença nos bairros, por meio de interpolação espacial. Foram detectadas áreas para intervenção em 15 bairros de Cartagena e 13 de Barranquilla, em 50 metros ao redor dos casos ativos com menos de 21 dias de evolução e de acordo com as camadas de risco geográfico, como mecanismo para impedir a propagação da COVID-19. Conclusões: A análise espacial permitiu determinar a estrutura temporal e espacial como uma metodologia complementar útil para o rastreamento de contatos, e forneceu a evidência científica necessária para a aplicação de medidas de intervenção direta, quando necessário, visando a reduzir o contágio pelo SARS-CoV-2.

On Fractional Differential Inclusion for an Epidemic Model via L-Fuzzy Fixed Point Results

The real world is filled with uncertainty, vagueness, and imprecision. The concepts we meet in everyday life are vague rather than precise. In real-world situations, if a model requires that conclusions drawn from it have some bearings on reality, then two major problems immediately arise, viz. real situations are not usually crisp and deterministic;complete descriptions of real systems often require more comprehensive data than human beings could recognize simultaneously, process and understand. Conventional mathematical tools which require all inferences to be exact, are not always efficient to handle imprecisions in a wide variety of practical situations. Following the latter development, a lot of attention has been paid to examining novel L-fuzzy analogues of conventional functional equations and their various applications. In this paper, new coincidence point results for single-valued mappings and an L-fuzzy set-valued map in metric spaces are proposed. Regarding novelty and generality, the obtained invariant point notions are compared with some well-known related concepts via non-trivial examples. It is observed that our principal results subsume and refine some important ones in the corresponding domains. As an application, one of our results is utilized to discuss more general existence conditions for realizing the solutions of a non-integer order inclusion model for COVID-19.

Covid-19 Control: Face Mask Detection Using Deep Learning for Balanced and Unbalanced Dataset

Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure;dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.