Analyzing the Implications of COVID-19 Pandemic through an Intelligent-Computing Technique

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the World Health Organization (WHO) on March 11, 2020. COVID-19 has already affected more than 211 nations. In such a bleak scenario, it becomes imperative to analyze and identify those regions in Saudi Arabia that are at high risk. A preemptive study done in the context of predicting the possible COVID-19 hotspots would facilitate in the implementation of prompt and targeted countermeasures against SARS-CoV-2, thus saving many lives. Working towards this intent, the present study adopts a decision making based methodology of simulation named Analytical Hierarchy Process (AHP), a multi criteria decision making approach, for assessing the risk of COVID-19 in different regions of Saudi Arabia. AHP gives the ability to measure the risks numerically. Moreover, numerical assessments are always effective and easy to understand. Hence, this research endeavour employs Fuzzy based computational method of decision making for its empirical analysis. Findings in the proposed paper suggest that Riyadh and Makkah are the most susceptible regions, implying that if sustained and focused preventive measures are not introduced at the right juncture, the two cities could be the worst afflicted with the infection. The results obtained through Fuzzy based computational method of decision making are highly corroborative and would be very useful for categorizing and assessing the current COVID-19 situation in the Kingdom of Saudi Arabia. More specifically, identifying the cities that are likely to be COVID-19 hotspots would help the country’s health and medical fraternity to reinforce intensive containment strategies to counter the ills of the pandemic in such regions.     

COVID-19 Pandemic Data Predict the Stock Market

Unlike the 2007–2008 market crash, which was caused by a banking failure and led to an economic recession, the 1918 influenza pandemic triggered a worldwide financial depression. Pandemics usually affect the global economy, and the COVID-19 pandemic is no exception. Many stock markets have fallen over 40%, and companies are shutting down, ending contracts, and issuing voluntary and involuntary leaves for thousands of employees. These economic effects have led to an increase in unemployment rates, crime, and instability. Studying pandemics’ economic effects, especially on the stock market, has not been urgent or feasible until recently. However, with advances in artificial intelligence (AI) and the inter-connectivity that social media provides, such research has become possible. In this paper, we propose a COVID-19-based stock market prediction system (C19-SM2) that utilizes social media. Our AI system enables economists to study how COVID-19 pandemic data influence social media and, hence, the stock market. C19-SM2 gathers COVID-19 infection and death cases reported by the authorities and social media data from a geographic area and extracts the sentiments and events that occur in that area. The information is then fed to the support vector machine (SVM) and random forest and random tree classifiers along with current stock market values. Then, the system produces a projection of the stock market’s movement during the next day. We tested the system with the Dow Jones Industrial Average (DJI) and the Tadawul All Share Index (TASI). Our system achieved a stock market prediction accuracy of 99.71%, substantially higher than the 89.93% accuracy reported in the related literature; the inclusion of COVID-19 data improved accuracy by 9.78%.     

Learning Patterns from COVID-19 Instances

Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article also explores traditional, and Deep Learning (DL) approaches for COVID-19 patterns from Chest X-Ray images to predict, analyze, and further understand this virus. The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases. In contrast, for COVID-19 versus Pneumonia Virus scenario, we achieve 94.5% accurate detections. Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks. Thus, the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.     

A literature survey of the robotic technologies during the COVID-19 pandemic

Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies’ perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies’ perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work’s technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.     

COVID‐19疫情下基于YOLOv4的安全社交距离风险评估

为保持行人在新型冠状病毒肺炎（COVID-19）疫情下的安全社交距离,有效控制和预防疫情传播,构建一种基于YOLOv4的安全社交距离风险评估模型。利用微调后的YOLOv4算法对行人进行目标提取,获取行人关键点,并将行人连续运动视为质点的连续运动,结合DeepSort算法实现对行人的跟踪处理。在此基础上,建立视觉坐标系,在鸟瞰视角下提出运动矢量分析算法计算和判断行人运动方向并评估行人的安全社交距离。在牛津城市中心的数据集上评估模型有效性,实验结果表明,微调后YOLOv4算法在行人检测中平均精度均值达到90.33%,行人社交距离风险评估准确率达到88.23%,性能优于Fast R-CNN、Faster R-CNN、YOLOv3和YOLOv4算法,表明所提模型能够有效提升安全社交距离的检测准确性。     

Prediction of COVID-19 Pandemic Spread in Kingdom of Saudi Arabia

A significant increase in the number of coronavirus cases can easily be noticed in most of the countries around the world. Inspite of the consistent preventive initiatives being taken to contain the spread of this virus, the unabated increase in the cases is both alarming and intriguing. The role of mathematical models in predicting and estimating the spread of the virus, and identifying various preventive factors dependencies has been found important and effective in most of the previous pandemics like Severe Acute Respiratory Syndrome (SARS) 2003. In this research work, authors have proposed the Susceptible-Infectected-Removed (SIR) model variation in order to forecast the pattern of coronavirus disease (COVID-19) spread for the upcoming eight weeks in perspective of Saudi Arabia. The study has been performed by using SIR model with a proposed simplification using average progression for further estimation of β and γ values for better curve fittings ratios. The predictive results of this study clearly show that under the current public health interventions, there will be an increase in the COVID-19 cases in Saudi Arabia in the next four weeks. Hence, a set of strong health primitives and precautionary measures are recommended in order to avoid and prevent the further spread of COVID-19 in Saudi Arabia.     

Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020, provide critical research criteria to assess the vulnerabilities of our current health system. The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness, about public health and healthcare mechanisms. In view of this unprecedented health crisis, distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages, and with the higher efficacy. At the implementation level, blockchain integration, early detection and avoidance of an outbreak, identity protection and safety, and a secure drug supply chain can be realized. At the opposite end of the continuum, artificial intelligence methods are used to detect corona effects until they become too serious, avoiding costly drug processing. The paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic scenarios. This paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational factors. This paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.     

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.     

新冠肺炎疫情下的本科线上教学方案与实践

新冠肺炎疫情使得全国高校开展了大规模的线上教学活动。该文阐述了在疫情形势下开展线上教学的方案与实践。     

Crowd management COVID-19

Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people’s mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user’s choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.     

改进U-Net的新冠肺炎图像分割方法

新型冠状病毒肺炎(COVID-19)大流行疾病正在全球范围内蔓延.计算机断层扫描(CT)影像技术,在抗击全球COVID-19的斗争中起着至关重要的作用,诊断新冠肺炎时,如果能够从CT图像中自动准确分割出新冠肺炎病灶区域,将有助于医生进行更准确和快速的诊断.针对新冠肺炎病灶分割问题,提出基于U-Net改进模型的自动分割方...     

超级计算支撑的新冠肺炎CT影像综合分析辅助系统应用

目的 新冠肺炎（COVID-19）已经成为全球大流行疾病,在全球范围数百万人确诊。基于计算机断层扫描（computed tomography,CT）数据的影像学分析是临床诊断的重要手段。为了实现快速高效高精度地检测,提出了一种超级计算支撑的新冠肺炎CT影像综合分析辅助系统构建方法。方法 系统整个处理流程依次包括输入处理模块、预处理模块、影像学分析子系统和人工智能（artifiaial intelligence,AI）分析子系统4部分。其中影像学分析子系统通过分析肺实变、磨玻璃影和铺路石等影像学典型特征检测是否有肺炎和典型新冠肺炎特征,给出肺炎影像分析结论;AI分析子系统通过构建深度学习模型来区分普通病毒肺炎与新冠肺炎,增加肺炎的筛查甄别能力。结果 系统发布以来,持续稳定地为国内外超过三十家医院与一百多家科研机构提供了新冠肺炎辅助诊断服务和科研支撑,为抗击疫情提供重要支撑。结论 本文提出的超级计算支撑的新冠肺炎CT影像综合分析辅助系统构建方法,取得了应用效果,是一种有效实现快速部署服务、对突发疫情提供高效支撑的服务方式。     

Emergency decision support modeling for COVID-19 based on spherical fuzzy information

Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision-makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID-19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision-making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.     

基于SEIR-ARIMA混合模型的新冠肺炎预测

新型冠状病毒肺炎简称新冠肺炎,是一种由新型冠状病毒引起的急性感染性肺炎,具有传染性强、人群普遍易感的特点。因此,对新冠肺炎感染人数的预测,不仅仅有利于国家面对疫情做出科学决策,而且有利于及时整合防疫资源。本文提出一种基于传统的传染病动力模型SEIR和差分整合移动平均自回归模型ARIMA构建的SEIR-ARIMA混合模型,对不同时间段、不同地点的新冠肺炎疫情做出预测和分析。从实验结果上看,基于SEIR-ARIMA混合模型的预测,比常见的用于新冠肺炎预测的逻辑回归Logistic、长短期记忆人工神经网络LSTM、SEIR模型、ARIMA模型有较好的预测效果。为了真实地反映出实验效果的提高是否源于SEIR与ARIMA模型结合的优势,本文还实现SEIR-Logistic混合模型和SEIR-LSTM混合模型,并与SEIR-ARIMA对比分析得出,SEIR-ARIMA预测都取得更好的预测效果。因此,基于SEIR-ARIMA混合模型对新冠肺炎的发展趋势的分析相对可靠,有利于国家面对疫情的科学决策,对我国未来预防其他类型的传染病具有很好的应用价值。     

Sentiment analysis on Hindi tweets during COVID-19 pandemic

A gap among the people has been created due to a lack of social interactions. The physical void has led to an increase in online interaction among users on social media platforms. Sentiment analysis of such interactions can help us analyze the general public psychology during the pandemic. However, the lack of data in non-English and low-resource languages like ‘Hindi’ makes it difficult to study it among native and non-English speaking masses. Here, we create a small collection of ‘Hindi’ tweets on COVID-19 during the pandemic containing 10,011 tweets for sentiment analysis, which is named as sentiment analysis for Hindi (SAFH). In this article, we describe the process of collecting, creating, annotating the corpus, and sentiment classification. The claims have been verified using different word embedding with a deep learning classifier through the proposed model. The achieved accuracy of the proposed model yields up to a permissible rate of 90.9%.     

面向COVID-19疫情预测的图卷积神经网络时空数据学习

目的 当前的疾病传播研究主要集中于时序数据和传染病模型,缺乏运用空间信息提升预测精度的探索和解释。在处理时空数据时需要分别提取时间特征和空间特征,再进行特征融合得到较为可靠的预测结果。本文提出一种基于图卷积神经网络（graph convolutional neural network,GCN）的时空数据学习方法,能够运用空间模型端对端地学习时空数据,代替此前由多模块单元相集成的模式。方法 依据数据可视化阶段呈现出的地理空间、高铁线路、飞机航线与感染人数之间的正相关关系,将中国各城市之间的空间分布关系和交通连接关系映射成网络图并编码成地理邻接矩阵、高铁线路直达矩阵、飞机航线直达矩阵以及飞机航线或高铁线路直达矩阵。按滑动时间窗口对疫情数据进行切片后形成张量,依次分批输入到图深度学习模型中参与卷积运算,通过信息传递、反向传播和梯度下降更新可训练参数。结果 在新型冠状病毒肺炎疫情数据集上的实验结果显示,采用GCN学习这一时空数据的分布特征相较于循环神经网络模型,在训练过程中表现出了更强的拟合能力,在训练时间层面节约75%以上的运算成本,在两类损失函数下的平均测试集损失能够下降80%左右。结论 本文所采用的时空数据学习方法具有较低的运算成本和较高的预测精度,尤其在空间特征强于时间特征的时空数据中有着更好的性能,并且为流行病传播范围和感染人数的预测提供了新的方法和思路,有助于相关部门在公共卫生事件中制定应对措施和疾病防控决策。     

Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19

The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.     

Organizational decision-making during COVID-19: A qualitative analysis of the organizational decision-making system in the United States during COVID-19

This study sought to understand COVID-19-related organizational decisions were made across sectors. To gain this understanding, we conducted semi-structured interviews with organizational decision-makers in North Carolina about their experiences responding to COVID-19. Conventional content analysis was used to analyse the context, inputs, and processes involved in decision-making. Between October 2020 and February 2021, we interviewed 44 decision-makers from the following sectors: business (n = 4), community non-profit (n = 3), county government (n = 4), healthcare (n = 5), local public health (n = 5), public safety (n = 7), religious (n = 6), education (n = 7) and transportation (n = 3). We found that during the pandemic, organizations looked to scientific authorities, the decisions of peer organizations, data about COVID-19, and their own experience with prior crises. Interpretation of inputs was informed by current political events, societal trends, and organization mission. Decision-makers had to account for divergent internal opinions and community behaviour. To navigate inputs and contextual factors, organizations decentralized decision-making authority, formed auxiliary decision-making bodies, learned to resolve internal conflicts, learned in real time from their crisis response, and routinely communicated decisions with their communities. In conclusion, aligned with systems and contingency theories of decision-making, decision-making during COVID-19 depended on an organization's ‘fit’ within the specifics of their existing system and their ability to orient the dynamics of that system to their own goals.     

Deep Convolutional Neural Network Approach for COVID-19 Detection

Coronavirus disease 2019 (Covid-19) is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses. As by the end of 2020, Covid-19 is still not fully understood, but like other similar viruses, the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons. The accurate detection of Covid-19 cases poses some questions to scientists and physicians. The two main kinds of tests available for Covid-19 are viral tests, which tells you whether you are currently infected and antibody test, which tells if you had been infected previously. Routine Covid-19 test can take up to 2 days to complete; in reducing chances of false negative results, serial testing is used. Medical image processing by means of using Chest X-ray images and Computed Tomography (CT) can help radiologists detect the virus. This imaging approach can detect certain characteristic changes in the lung associated with Covid-19. In this paper, a deep learning model or technique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray images. The entire model proposed is categorized into three stages: dataset, data pre-processing and final stage being training and classification.     

基于深度学习的COVID-19智能诊断系统

本研究旨在探索运用深度学习的方法辅助医生利用胸部X光片进行COVID-19诊断的可行性和准确性。首先利用公开的COVID-QU-Ex Dataset训练集训练一个UNet分割模型,实现肺部ROI区域的自动分割。其次完成对该公共数据集肺部区域的自动提取预处理。再次利用预处理后的三分类影像数据（新冠肺炎、其它肺炎、正常）采用迁移学习的方式训练了一个分类模型MBCA-COVIDNET,该模型以MobileNetV2作为骨干网络,并在其中加入坐标注意力机制（CA）。最后利用训练好的模型和Hugging Face开源软件搭建了一套方便医生使用的COVID-19智能辅助诊断系统。该模型在COVID-QU-Ex Dataset测试集上取得了高达97.98%的准确率,而该模型的参数量和MACs仅有2.23M和0.33G,易于在硬件设备上进行部署。该智能诊断系统能够很好的辅助医生进行基于胸片的COVID-19诊断,提升诊断的准确率以及诊断效率。