Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Applied Intelligence - Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benefits of social data analysis for the...     

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms

Multimedia Tools and Applications - While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for...     

环境感知的新冠肺炎传播态势的建模与分析

针对现有传播模型没有考虑个体所在环境对个体感染病毒的影响以及经典传播模型无法很好地刻画个体特征的问题,提出了基于环境感知的病毒传播模型(EA-SIR).首先,引入个体接触矩阵来描述个体之间的接触情况,推导基于环境感知的个体染病概率,建立EA-SIR的微分方程组;然后,推导EA-SIR的基本再生数及其上界.EA-SIR的基本再生数上界小于或等于经典SIR模型,说明病毒在EA-SIR中更难传播,实际上人们强烈的自我保护意识有利于阻断病毒的传播,因此理论上EA-SIR更适用于刻画病毒的传播.最后,使用"钻石公主"号以及国内四个城市的疫情数据进行实验,以平均绝对误差(MAE)为评价标准.仿真实验的结果表明,EA-SIR能够较好地刻画新冠肺炎疫情的传播态势.     

环境感知的新冠肺炎传播态势的建模与分析

针对现有传播模型没有考虑个体所在环境对个体感染病毒的影响以及经典传播模型无法很好地刻画个体特征的问题,提出了基于环境感知的病毒传播模型(EA-SIR).首先,引入个体接触矩阵来描述个体之间的接触情况,推导基于环境感知的个体染病概率,建立EA-SIR的微分方程组;然后,推导EA-SIR的基本再生数及其上界.EA-SIR的基本再生数上界小于或等于经典SIR模型,说明病毒在EA-SIR中更难传播,实际上人们强烈的自我保护意识有利于阻断病毒的传播,因此理论上EA-SIR更适用于刻画病毒的传播.最后,使用钻石公主号以及国内四个城市的疫情数据进行实验,以平均绝对误差(MAE)为评价标准.仿真实验的结果表明,EA-SIR能够较好地刻画新冠肺炎疫情的传播态势.     

Unsupervised domain adaptation based COVID-19 CT infection segmentation network

Applied Intelligence - Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnostic approach for COVID-19. However, due to the limited...     

A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak

Applied Intelligence - Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the...     

COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis

Aim: COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images.Methods: Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet.Results: On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods.Conclusions: CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.     

COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images

COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions.     

Using artificial intelligence techniques for COVID-19 genome analysis

Applied Intelligence - The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province,...     

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%.     

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。     

Design and analysis of a large-scale COVID-19 tweets dataset

As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset’s geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets’ design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively.

     

COVID-19 epidemic control using short-term lockdowns for collective gain

While many efforts are currently devoted to vaccines development and administration, social distancing measures, including severe restrictions such as lockdowns, remain fundamental tools to contain the spread of COVID-19. A crucial point for any government is to understand, on the basis of the epidemic curve, the right temporal instant to set up a lockdown and then to remove it. Different strategies are being adopted with distinct shades of intensity. USA and Europe tend to introduce restrictions of considerable temporal length. They vary in time: a severe lockdown may be reached and then gradually relaxed. An interesting alternative is the Australian model where short and sharp responses have repeatedly tackled the virus and allowed people a return to near normalcy. After a few positive cases are detected, a lockdown is immediately set. In this paper we show that the Australian model can be generalized and given a rigorous mathematical analysis, casting strategies of the type short-term pain for collective gain in the context of sliding-mode control, an important branch of nonlinear control theory. This allows us to gain important insights regarding how to implement short-term lockdowns, obtaining a better understanding of their merits and possible limitations. Effects of vaccines administration in improving the control law’s effectiveness are also illustrated. Our model predicts the duration of the severe lockdown to be set to maintain e.g. the number of people in intensive care under a certain threshold. After tuning our strategy exploiting data collected in Italy, it turns out that COVID-19 epidemic could be e.g. controlled by alternating one or two weeks of complete lockdown with one or two months of freedom, respectively. Control strategies of this kind, where the lockdown’s duration is well circumscribed, could be important also to alleviate coronavirus impact on economy.     

Multicriteria decision analysis for sustainable data centers location

Data centers (DCs) are complex organizational and technical infrastructures that assure the performance and reliability of modern information and communication systems. The high installation and operations costs of DCs and the stringent requirements regarding reliability and safety require close attention to the location of this type of facility. This paper proposes a multicriteria decision analysis (MCDA) approach for identifying the most interesting locations to install sustainable DCs, taking into account technical, social, economic, and environmental dimensions. For each of these main dimensions, the evaluation was formulated as a multicriteria sorting problem. These problems were analyzed using the outranking MCDA method ELECTRE TRI through the IRIS software, allowing for uncertainty about the criteria weights. The results are summarized in a graphical form, without attempting to reduce such incommensurable dimensions to a single value.     

结构图注意力网络的新冠肺炎轻重症诊断

目的 为辅助医生快速分辨新型冠状病毒肺炎(corona virus disease 2019, COVID-19)轻、重症患者,以便对症下药减轻医疗负担,提出一种基于结构图注意力网络的轻重症诊断算法。方法 基于胸部CT图像提取的特定特征以及肺段间的位置关系构建结构图,以肺部内不同肺段为节点,以提取特征为节点属性。采用图神经网络汇聚相邻节点特征,再利用池化层获取分别代表左肺叶和右肺叶特征的图表示。使用结构注意力机制计算左、右肺叶的感染情况对结果诊断的重要性,并依据重要性融合左、右肺叶图表示以得到最终图表示,最后执行分类任务。由于数据中存在明显的类别不平衡现象,采用Focal-Loss损失函数优化模型以减轻对分类结果的影响。结果 实验将所提算法分别与传统机器学习方法和流行的图神经网络算法做性能对比。在重症诊断的准确率上,本文算法相较于传统机器学习方法和图神经网络算法分别取得14.2%～42.0%和3.6%～4.8%的提升。在AUC(area under curve)指标上,本文算法相较于上述两种算法分别取得8.9%～18.7%和3.1%～3.6%的提升。除此之外,通过消融实验发现具有结构注...     

Transfer learning for the efficient detection of COVID-19 from smartphone audio data

Disease detection from smartphone data represents an open research challenge in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are an important case study in this area and their early detection is a potential real instrument to counteract the pandemic situation. The efficacy of this solution mainly depends on the performances of AI algorithms applied to the collected data and their possible implementation directly on the users’ mobile devices. Considering these issues, and the limited amount of available data, in this paper we present the experimental evaluation of 3 different deep learning models, compared also with hand-crafted features, and of two main approaches of transfer learning in the considered scenario: both feature extraction and fine-tuning. Specifically, we considered VGGish, YAMNET, and L³-Net (including 12 different configurations) evaluated through user-independent experiments on 4 different datasets (13,447 samples in total). Results clearly show the advantages of L³-Net in all the experimental settings as it overcomes the other solutions by 12.3% in terms of Precision–Recall AUC as features extractor, and by 10% when the model is fine-tuned. Moreover, we note that to fine-tune only the fully-connected layers of the pre-trained models generally leads to worse performances, with an average drop of 6.6% with respect to feature extraction. Finally, we evaluate the memory footprints of the different models for their possible applications on commercial mobile devices.     

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.     

Sentiment analysis and topic modeling for COVID-19 vaccine discussions

World Wide Web - The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world....     

The analysis of isolation measures for epidemic control of COVID-19

This paper proposes a susceptible exposed infectious recovered model (SEIR) with isolation measures to evaluate the COVID-19 epidemic based on the prevention and control policy implemented by the Chinese government on February 23, 2020. According to the Chinese government’s immediate isolation and centralized diagnosis of confirmed cases, and the adoption of epidemic tracking measures on patients to prevent further spread of the epidemic, we divide the population into susceptible, exposed, infectious, quarantine, confirmed and recovered. This paper proposes an SEIR model with isolation measures that simultaneously investigates the infectivity of the incubation period, reflects prevention and control measures and calculates the basic reproduction number of the model. According to the data released by the National Health Commission of the People’s Republic of China, we estimated the parameters of the model and compared the simulation results of the model with actual data. We have considered the trend of the epidemic under different incubation periods of infectious capacity. When the incubation period is not contagious, the peak number of confirmed in the model is 33,870; and when the infectious capacity is 0.1 times the infectious capacity in the infectious period, the peak number of confirmed in the model is 57,950; when the infectious capacity is doubled, the peak number of confirmed will reach 109,300. Moreover, by changing the contact rate in the model, we found that as the intensity of prevention and control measures increase, the peak of the epidemic will come earlier, and the peak number of confirmed will also be significantly reduced. Under extremely strict prevention and control measures, the peak number of confirmed cases has dropped by nearly 50%. In addition, we use the EEMD method to decompose the time series data of the epidemic, and then combine the LSTM model to predict the trend of the epidemic. Compared with the method of directly using LSTM for prediction, more detailed information can be obtained.

     

Coronavirus disease 2019 (COVID-19): survival analysis using deep learning and Cox regression model

Pattern Analysis and Applications - Coronavirus (COVID-19) is one of the most serious problems that has caused stopping the wheel of life all over the world. It is widely spread to the extent that...