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对新冠肺炎疫情数据进行可视分析,可以直观展示疫情动态、挖掘疫情传播规律、预测疫情发展趋势.以开源渠道获取的多维时空新冠肺炎疫情数据为基础,针对疫情数据的多维时空特征构建病例数量数据集、病例来源数据集和病例关系数据集.在数据预处理的基础上,综合应用时间轴交互、流行病数学模型等分析方法,提出一个新冠肺炎疫情可视化模型,采用递进式分析的方法对典型传染病疫情数据进行可视分析.以河南省的疫情数据为例,展示了新冠肺炎疫情态势,挖掘了新冠肺炎疫情来源特征,总结了新冠肺炎疫情传播模式,预测了新冠肺炎疫情未来趋势,为河南省新冠疫情防控提供了科学依据. 相似文献
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After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The results revealed that during COVID-19, the proposed integrated model, which trained both the Twitter sentiment values and historical VIX values, presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models. 相似文献
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Social distancing as a form of nonpharmaceutical intervention has been enacted in many countries as a form of mitigating the spread of COVID-19. There has been a large interest in mathematical modeling to aid in the prediction of both the total infected population and virus-related deaths, as well as to aid government agencies in decision making. As the virus continues to spread, there are both economic and sociological incentives to minimize time spent with strict distancing mandates enforced, and/or to adopt periodically relaxed distancing protocols, which allow for scheduled economic activity. The main objective of this study is to reduce the disease burden in a population, here measured as the peak of the infected population, while simultaneously minimizing the length of time the population is socially distanced, utilizing both a single period of social distancing as well as periodic relaxation. We derive a linear relationship among the optimal start time and duration of a single interval of social distancing from an approximation of the classic epidemic SIR model. Furthermore, we see a sharp phase transition region in start times for a single pulse of distancing, where the peak of the infected population changes rapidly; notably, this transition occurs well before one would intuitively expect. By numerical investigation of more sophisticated epidemiological models designed specifically to describe the COVID-19 pandemic, we see that all share remarkably similar dynamic characteristics when contact rates are subject to periodic or one-shot changes, and hence lead us to conclude that these features are universal in epidemic models. On the other hand, the nonlinearity of epidemic models leads to non-monotone behavior of the peak of infected population under periodic relaxation of social distancing policies. This observation led us to hypothesize that an additional single interval social distancing at a proper time can significantly decrease the infected peak of periodic policies, and we verified this improvement numerically. While synchronous quarantine and social distancing mandates across populations effectively minimize the spread of an epidemic over the world, relaxation decisions should not be enacted at the same time for different populations. 相似文献
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We investigate adaptive strategies to robustly and optimally control the COVID-19 pandemic via social distancing measures based on the example of Germany. Our goal is to minimize the number of fatalities over the course of two years without inducing excessive social costs. We consider a tailored model of the German COVID-19 outbreak with different parameter sets to design and validate our approach. Our analysis reveals that an open-loop optimal control policy can significantly decrease the number of fatalities when compared to simpler policies under the assumption of exact model knowledge. In a more realistic scenario with uncertain data and model mismatch, a feedback strategy that updates the policy weekly using model predictive control (MPC) leads to a reliable performance, even when applied to a validation model with deviant parameters. On top of that, we propose a robust MPC-based feedback policy using interval arithmetic that adapts the social distancing measures cautiously and safely, thus leading to a minimum number of fatalities even if measurements are inaccurate and the infection rates cannot be precisely specified by social distancing. Our theoretical findings support various recent studies by showing that (1) adaptive feedback strategies are required to reliably contain the COVID-19 outbreak, (2) well-designed policies can significantly reduce the number of fatalities compared to simpler ones while keeping the amount of social distancing measures on the same level, and (3) imposing stronger social distancing measures early on is more effective and cheaper in the long run than opening up too soon and restoring stricter measures at a later time. 相似文献
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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。 相似文献
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Abdulaziz Attaallah Sabita Khatri Mohd Nadeem Syed Anas Ansar Abhishek Kumar Pandey Alka Agrawal 《计算机系统科学与工程》2021,37(3):313-329
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. 相似文献
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基于ARMAX模型自适应预测函数控制 总被引：10，自引：0，他引：10
本文提出了基于ARMAX模型的自适应预测函数控制,该算法的特点是占用内存少,计算速度快,并具有较强的鲁棒性．ARMAX模型参数是通过带遗忘因子的递推最小二乘算法在线辨识得到．仿真结果表明,该控制算法比PID控制具有更好的控制品质． 相似文献
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Abdulaziz Almehmadi 《计算机系统科学与工程》2021,36(3):451-460
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%. 相似文献
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Elder M. Hemerly 《Systems & Control Letters》1990,14(1)
The general formula of the PLS (Predictive Least Squares) criterion for order estimation is worked out under the assumption that the parameter estimates are calculated via the AML (Approximate Maximum Likelihood). A particular case is then carefully analysed and it is shown that depending on the system generating the data the PLS critetion using the a posteriori prediction error can, surprisingly, almost surely overestimate the true order. 相似文献
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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. 相似文献
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In this paper, a new version of the well-known epidemic mathematical SEIR model is used to analyze the pandemic course of COVID-19 in eight different countries. One of the proposed model’s improvements is to reflect the societal feedback on the disease and confinement features. The SEIR model parameters are allowed to be time-varying, and the ranges of their values are identified by using publicly available data for France, Italy, Spain, Germany, Brazil, Russia, New York State (US), and China. The identified model is then applied to predict the SARS-CoV-2 virus propagation under various conditions of confinement. For this purpose, an interval predictor is designed, allowing variations and uncertainties in the model parameters to be taken into account. The code and the utilized data are available on Github. 相似文献
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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。 相似文献
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基于多模型的动态矩阵控制 总被引：2，自引：2，他引：2
针对动态特性随工况而变化的复杂工业过程,本文提出一种基于多模型的动态矩阵控制。根据被控对象参数的变化范围建立多个模型,同时建立以模型输出误差为变量的具有积分特点的指标切换函数。在每一采样时刻根据指标函数与实际对象最接近的模型,并将基于此模型的控制器切换为当前控制器。仿真结果表明对多工况的控制令人满意。 相似文献
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This paper presents a novel combination of the hidden Markov model (HMM) and the fuzzy models for forecasting stock market data. In a previous study we used an HMM to identify similar data patterns from the historical data and then used a weighted average to generate a ‘one-day-ahead’ forecast. This paper uses a similar approach to identify data patterns by using the HMM and then uses fuzzy logic to obtain a forecast value. The HMM's log-likelihood for each of the input data vectors is used to partition the dataspace. Each of the divided dataspaces is then used to generate a fuzzy rule. The fuzzy model developed from this approach is tested on stock market data drawn from different sectors. Experimental results clearly show an improved forecasting accuracy compared to other forecasting models such as, ARIMA, artificial neural network (ANN) and another HMM-based forecasting model. 相似文献
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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。 相似文献
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盛华芳 《计算机工程与应用》2020,56(17):33-40
提出了一种基于有效再生数的大型体育赛事重启评估的方法.将疾病传播有效再生数Rt作为衡量体育赛事重启安全评估的关键系数,并进行安全分级.对比分析引入无症状感染者的SEIAR模型和改进后的引入戴口罩的Wells-Riley模型,采用前者对地区的疫情进行初步评估,采用后者对体育赛事场馆的疾病传播性进行评估.采用Gaussia... 相似文献