ABSTRACT
Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.
ABSTRACT
The sudden outbreak of COVID-19 has created dramatic challenges for public health and textile export trade worldwide. Such abrupt changes are difficult to predict due to the inherently high complexity and nonlinearity, especially with limited data. This article proposes a novel modified discrete grey model with weakening buffer operators, called BODGM (1,1), for forecasting the impact of pandemic-induced uncertainty on the volatility of cotton exports in China under limited samples. First, the Mann-Kendall test examines how pandemic-induced uncertainty affects cotton exports, based on China's monthly cotton export data from June 2014 to August 2022. Second, buffer operators are employed to weaken the nonlinear trends and correct the tentative predictions of the discrete grey model. Then, the BODGM (1,1) model was validated by comparison with four alternative models. The results indicate that the BODGM (1,1) model was particularly promising for identifying mutational fluctuations in cotton exports and outperformed the GM (1,1), DGM (1,1), ARIMA and linear regression models in fitting and prediction accuracy under volatility and limited data. The BODGM (1,1) model forecast results for China showed that cotton export volume was expected to show signs of recovery over the next 12 months. The findings of this study may provide a basis for formulating trade policies to mitigate the impact of the COVID-19 outbreak on export resources and build their resilience to future pandemics.
ABSTRACT
COVID-19 has dramatically changed people's mobility patterns. This report aims to analyze the impact of COVID-19 on people's mobility through statistics and comparing the visits of POIs (Point-Of-Interests) in New York State in 2019 and 2020. The report uses data from SafeGraph, which is a data company. The raw data contains POI visits across the United States in 2019 and 2020. Considering the analysis size and difficulty of the data, POI visits from New York State are extracted for analysis, and POI locations are classified according to the tags provided by the source data. The scale of analysis is from macro to micro, and they are the total POI visits data of New York State based on different ways in 2019 and 2020, the POI visits of CBG (Census Block Group) division in New York City, and three representative POI samples to do individual analysis. The analysis methods are: (1) use line plot and bar plot statistics to compare the trends of POI visits data from 2019 to 2020, and (2) make the spatial visualization comparison, which includes grid map, scatter map, heatmap, and OD map, between the first peak of epidemic impact in the first full week of April 2019 and April 2020, and the scope is narrowed to New York City. Wherein the OD maps are drawn based on the CBG division. Compared to related work, the analysis object includes CBG, categories, and individual POI. In addition, the analysis method combines statistical graphs and spatial visualizations and explores the policy impact of the New York City government. This report adopts more multidimensional analysis methods and objects to improve the comprehensiveness and reliability of the analysis content. © 2022 IEEE.
ABSTRACT
COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on real-world human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers. © 2022 held by the owner/author(s). Publication rights licensed to ACM.
ABSTRACT
Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimating the time/region-varying epidemiological parameters, while most deep learning models lack the guidance of epidemiological domain knowledge and interpretability of prediction results. In this study, we propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting by incorporating Graph Neural Networks (GNNs) and graph learning mechanisms into Metapopulation SIR model. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters and the underlying epidemic propagation graph from heterogeneous data in an end-to-end manner. Experiment results demonstrate our model outperforms the existing mechanistic models and deep learning models by a large margin. Furthermore, the analysis on the learned parameters demonstrates the high reliability and interpretability of our model and helps better understanding of epidemic spread. Our model and data have already been public on GitHub https://github.com/deepkashiwa20/MepoGNN.git. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
At present, severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)infection is still rampant worldwide.As of September 10, 2021, there were about 222 million confirmed cases of corona virus disease 2019(COVID-19)and more than 4.6 million deaths worldwide.With the development of COVID-19 vaccines and the gradual vaccination worldwide, the increasing number of cases in children and unvaccinated young people has drawn attention.According to World Health Organization surveillance data, the proportion of COVID-19 infection cases in children gradually increased, and the proportion of cases in the age groups of under 5 years and 5-14 years increased from 1.0% and 2.5% in January 2020 to 2.0% and 8.7% in July 2021, respectively.At present, billions of adults have been vaccinated with various COVID-19 vaccines worldwide, and their protective effects including reducing infection and transmission, reducing severe disease and hospitalization, and reducing death, as well as high safety have been confirmed.Canada, the United States, Europe and other countries have approved the emergency COVID-19 vaccination in children and adolescents aged 12 to 17 years, and China has also approved the phased vaccination of COVID-19 vaccination in children and adolescents aged 3 to 17 years. For smooth advancement and implementation of COVID-19 vaccination in children, academic institutions, including National Clinical Research Center for Respiratory Diseases, National Center for Children's Health, and The Society of Pediatrics, Chinese Medical Association organized relevant experts to reach this consensus on COVID-19 vaccination in children.Copyright © 2021 by the Chinese Medical Association.
ABSTRACT
Severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)infection is still worldwide.As a vulnerable group, severe and dead pediatric cases are also reported.Under this severe epidemic situation, children should be well protected.With the widespread vaccination of SARS-CoV-2 vaccine in adults, the infection rate have decreased.Therefore, SARS-CoV-2 vaccine inoculation for children groups step by step is of great significance to the protection of children and the prevention and control of corona virus disease 2019(COVID-19) as a whole.But the safety of children vaccinated with SARS-CoV-2 vaccine is a main concern of parents.Therefore, in order to ensure the safety of vaccination and the implementation of vaccination work, National Clinical Research Center for Respiratory Diseases, National Center for Children's Health and the Society of Pediatrics, Chinese Medical Association organized experts to interpret the main issue of parents about SARS-CoV-2 vaccine for children, in order to answer the doubts of parents.Copyright © 2021 by the Chinese Medical Association.
ABSTRACT
2019 novel coronavirus(2019-nCoV) outbreak is one of the public health emergency of international concern.Since the 2019-nCoV outbreak, China has been adopting strict prevention and control measures, and has achieved remarkable results in the initial stage of prevention and control.However, some imported cases and sporadic regional cases have been found, and even short-term regional epidemics have occurred, indicating that the preventing and control against the epidemic remains grim.With the change of the incidence proportion and the number of cases in children under 18 years old, some new special symptoms and complications have appeared in children patients.In addition, with the occurrence of virus mutation, it has not only attracted attention from all parties, but also proposed a new topic for the prevention and treatment of 2019-nCoV infection in children of China.Based on the second edition, the present consensus further summarizes the clinical characteristics and experience of children's cases, and puts forward recommendations on the diagnostic criteria, laboratory examination, treatment, prevention and control of children's cases for providing reference for further guidance of treatment of 2019-nCoV infection in children.Copyright © 2021 Chinese Medical Association
ABSTRACT
In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneu-monia, lung cancer, or TB. Thus, the early diagnosis of COVID-19 is critical since the disease can provoke patients' mortality. Chest X-ray (CXR) is commonly employed in healthcare sector where both quick and precise diagnosis can be supplied. Deep learning algorithms have proved extraordi-nary capabilities in terms of lung diseases detection and classification. They facilitate and expedite the diagnosis process and save time for the medical practitioners. In this paper, a deep learning (DL) architecture for multi-class classification of Pneumonia, Lung Cancer, tuberculosis (TB), Lung Opacity, and most recently COVID-19 is proposed. Tremendous CXR images of 3615 COVID-19, 6012 Lung opacity, 5870 Pneumonia, 20,000 lung cancer, 1400 tuberculosis, and 10,192 normal images were resized, normalized, and randomly split to fit the DL requirements. In terms of classification, we utilized a pre-trained model, VGG19 followed by three blocks of con-volutional neural network (CNN) as a feature extraction and fully connected network at the clas-sification stage. The experimental results revealed that our proposed VGG19 + CNN outperformed other existing work with 96.48 % accuracy, 93.75 % recall, 97.56 % precision, 95.62 % F1 score, and 99.82 % area under the curve (AUC). The proposed model delivered supe-rior performance allowing healthcare practitioners to diagnose and treat patients more quickly and efficiently.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).
ABSTRACT
The COVID-19 pandemic has forced teachers to implement fully online teaching. This study reviewed the popular technologies that are used in online learning, as well as the advantages and difficulties of applying fully online courses for formal education. Based on this research background, this study proposed a nested scaffolding design of an online course for 215 college students in China with the help of six technological tools, which effectively replaced face-to-face interactions and significantly improved the usage of the supporting learning platform. The inner-outer learning cycles supported by the technological tools improved the quality of the scaffolding conversations, reduced the scaffolding time cost that teachers had to expend and enhanced the effectiveness of the individualised scaffolding instructions.Implications for practice or policy:center dot First-year students' learning outcomes can be improved by the scaffolding support from Web 2.0 resource URLs, a small private online course, and EducCoder resources.center dot Course leaders should construct at least 3-5 stage-wise evaluations to deconstruct the big learning process into several observable learning cycles, making the Kolb (1984) cycles controllable.center dot Assessors may need to consider involving various exercises, such as quizzes, online experiments and synthesised tasks to facilitate students' learning.
ABSTRACT
The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.
ABSTRACT
COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on realworld human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers.
ABSTRACT
Objective: The COVID-19 pandemic has been a challenge for hospital medical staffs worldwide due to high volumes of patients acutely ill with novel syndromes and prevailing uncertainty regarding optimum supportive and therapeutic interventions. Additionally, the response to this crisis was driven by a plethora of nontraditional information sources, such as email chains, websites, non-peer-reviewed preprints, and press releases. Care patterns became idiosyncratic and often incorporated unproven interventions driven by these nontraditional information sources. This report evaluates the efforts of a health system to create and empower a multidisciplinary committee to develop, implement, and monitor evidence-based, standardized protocols for patients with COVID-19. Method(s): This report describes the composition of the committee, its scope, and its important interactions with the health system pharmacy and therapeutics committee, research teams, and other work groups planning other aspects of COVID-19 management. It illustrates how the committee was used to demonstrate for trainees the process and value of critically examining evidence, even in a chaotic environment. Result(s): Data show successful interventions in reducing excessive ordering of certain laboratory tests, reduction of nonrecommended therapies, and rapid uptake of evidence-based or guidelines-supported interventions. Conclusion(s): A multidisciplinary committee dedicated solely to planning, implementing, and monitoring standard approaches that eventually became evidence-based decision-making led to an improved focus on treatment options and outcomes for COVID-19 patients. Data presented illustrate the attainable success of a committee that is both adaptable and suitable for similar emergencies in the future. Copyright © 2022 Turner White Communications Inc.. All rights reserved.
ABSTRACT
There is a growing trend for people to perform work-outs at home due to the global pandemic of COVID-19 and the stay-at-home policy of many countries. Since a self-designed fitness plan often lacks professional guidance to achieve ideal outcomes, it is important to have an in-home fitness monitoring system that can track the exercise process of users. Traditional camera-based fitness monitoring may raise serious privacy concerns, while sensor-based methods require users to wear dedicated devices. Recently, researchers propose to utilize RF signals to enable non-intrusive fitness monitoring, but these approaches all require huge training efforts from users to achieve a satisfactory performance, especially when the system is used by multiple users (e.g., family members). In this work, we design and implement a fitness monitoring system using a single COTS mm Wave device. The proposed system integrates workout recognition, user identification, multi-user monitoring, and training effort reduction modules and makes them work together in a single system. In particular, we develop a domain adaptation framework to reduce the amount of training data collected from different domains via mitigating impacts caused by domain characteristics embedded in mm Wave signals. We also develop a GAN-assisted method to achieve better user identification and workout recognition when only limited training data from the same domain is available. We propose a unique spatialtemporal heatmap feature to achieve personalized workout recognition and develop a clustering-based method for concurrent workout monitoring. Extensive experiments with 14 typical workouts involving 11 participants demonstrate that our system can achieve 97% average workout recognition accuracy and 91% user identification accuracy. © 2022 IEEE.
ABSTRACT
COVID-19 pandemic spread quickly in Wuhan, China in December 2019. This destructive infection spread quickly all over the planet, causing enormous misfortunes of individuals and property. All over the planet, researchers, clinicians and legislatures are continually looking for new technologies to against the COVID-19 pandemic. The use of artificial intelligence (AI) innovation gives a better approach to battle the pandemic. This paper sums up the exploration and utilization of AI in forecast and avoidance of COVID-19 pandemics, and the possibility of AI innovation used to fight against the pandemic in the situation of smart city. © 2022, Springer Nature Switzerland AG.
ABSTRACT
Since 2019, the world has been seriously impacted by the global pandemic, COVID-19, with millions of people adversely affected. This is coupled with a trend in which the intensity and frequency of natural disasters such as hurricanes, wildfires, and earthquakes have increased over the past decades. Larger and more diverse communities have been negatively influenced by these disasters and they might encounter crises socially and/or economically, further exacerbated when the natural disasters and pandemics co-occurred. However, conventional disaster response and management rely on human surveys and case studies to identify these in-crisis communities and their problems, which might not be effective and efficient due to the scale of the impacted population. In this paper, we propose to utilize the data-driven techniques and recent advances in artificial intelligence to automate the in-crisis community identification and improve its scalability and efficiency. Thus, immediate assistance to the in-crisis communities can be provided by society and timely disaster response and management can be achieved. A novel framework of the in-crisis community identification has been presented, which can be divided into three subtasks: (1) community detection, (2) in-crisis status detection, and (3) community demand and problem identification. Furthermore, the open issues and challenges toward automated in-crisis community identification are discussed to motivate future research and innovations in the area.
ABSTRACT
Owing to the global lockdown caused by the pandemic of COVID-19, the electricity demand is greatly affected, and the electricity market is also constantly fluctuating. During the pandemic period, the prediction of electricity demand is crucial to the economy and power dispatching. In this study, we combine the pandemic data and government anti-pandemic policies data to predict the electricity demand of the Contiguous United States by using the artificial neural network and recurrent neural network. In addition, the linear regression method is used to forecast the thermal generation with total generation data. Some experiments have developed to verify the effectiveness of the model. Then the model is used to forecast electricity demand and thermal generation under different policies and pandemic development, and the result were analyzed. © 2021 IEEE
ABSTRACT
At present, the world economy is in recession, especially under the impact of the Covid-19 epidemic, China's economy has also been greatly impacted. In this context, the disposable personal income of residents has also declined to varying degrees. More and more people choose economical life. If they can buy a used car in good condition at a good price, they are less likely to buy a brand new one. Under such a consumption concept, China's demand for second-hand cars is increasing. However, although China's second-hand car industry has developed for more than 30 years and the market scale has gradually expanded, there are still many problems behind the prosperity of the second-hand car market. These problems have existed for a long time, leading to a lot of disputes, unhappiness, disappointment, and even threats to the lives of consumers. These long-term problems also affect the virtuous circle of the second-hand car market, and hinder the healthy development of China's economy to a certain extent. In the past, research work mainly focused on the role of new policies, relevant laws and vehicle management and traffic management functions, this paper introduces the blockchain technology, which has the advantages of non tampering, transparency and traceability. This paper attempts to use blockchain technology as an auxiliary means to solve the long-standing problems in the used car market. This paper proposes a framework of used car trading based on blockchain in cloud service environment, and explains the working principle of the framework. Finally, the future research work is prospected. © 2021 IEEE.