ABSTRACT
The COVID-19 Research Database is a public data platform. This platform is a result of private and public partnerships across industries to facilitate data sharing and promote public health research. We analyzed its linked database and examined claims of 2,850,831 unique persons to investigate the influence of demographic, socio-economic, and behavioral factors on telehealth utilization in the low-income population. Our results suggest that patients who had higher education, income, and full-time employment were more likely to use telehealth. Patients who had unhealthy behaviors such as smoking were less likely to use telehealth. Our findings suggest that interventions to bolster education, employment, and healthy behaviors should be considered to promote the use of telehealth services. © 2023 IEEE Computer Society. All rights reserved.
ABSTRACT
Based on the data of COVID-19, this paper establishes the FCSEIR model for the spread through data analysis and designs the related simulation software. Using the data from Shanghai, the spread of the virus was simulated and predicted, and the process from outbreak to control of this infectious disease was better analyzed.
ABSTRACT
Covid-19 has undeniably accelerated the process of world education informatization. How to evaluate result of On-line Teaching through scientific and effective means is an important issue in the development of on-line education. On the "JZ on-line"platform, this paper constructs an On-line Teaching Appraisal system of vocational education based on convolutional neural network algorithm, which mainly includes five parts: evaluation personnel management, evaluation index management, On-line Teaching evaluation, evaluation content and evaluation result analysis. Based on the content of an indicator Appraisal system of On-line Teaching, a convolution neural network On-line Teaching evaluation model is constructed. Through the analysis of the data results of the system test, the system can obtain scientific and objective On-line Teaching evaluation results, and can put forward teaching problems and suggestions. It can also further analyze the distribution of different disciplines, different majors and different courses in the overall On-line Teaching evaluation. The system error is small and the precision is high, which is helpful to improve the quality of on-line course construction and the professional quality of teachers' educational technology. © 2022 ACM.
ABSTRACT
Since 2020, the precedence of COVID-19 and its variants has made a significant impact on the global fashion industry and instigated a fundamental change in consumer purchasing behaviour. A series of lockdowns, travel restrictions, social distancing has forced consumers to rely on and adapt to online shopping methods, causing major branding retail companies to innovate new online based consumer interaction systems. Moreover, work from home has significantly cut down sales in formal wear, while outing restrictions and social distancing has further cut down demand for fast and luxury fashion. Consumer needs and preferences in turn have reoriented towards home comfort and athleisure wear, as well as essential wearables. However, the current online shopping platforms do not provide a way for consumers to specify their own needs and preferences which leads to dissatisfaction for consumers, uncertainty in consumer purchases resulting in high inventory risk for branding retailers, as well as suppliers. This paper introduces how consumer's needs and preferences can be linked with product performance on e-shopping platforms with the novel consumer interactive system "Fashion Big Data (FBD) API plugins". It will describe how the FBD API plugins can enable consumers to set their own needs and preferences;compare consumers' needs and preferences with certified product performance;and provide respective smart personalized product recommendations. The paper will demonstrate real business case examples with hand feel, skin feel and thermal wear comfort FBD API plugins from the EU Horizon 2020, Fashion Big Data Model (FBD B_MODEL) project. This will be followed by FBD B_MODEL business case partner and consumer feedback. © Textile Bioengineering and Informatics Symposium Proceedings 2022 - 15th Textile Bioengineering and Informatics Symposium, TBIS 2022.
ABSTRACT
In this work, we introduce Social Minder, a Big Data platform for Social Media monitoring that allows massive extraction of textual information, and stands on a modular and scalable architecture for efficient real-time and batch processing. This demo is oriented to present a use case that provides users with estimates of credibility for webpages linked in Social Media. Social Minder can serve multiple research and commercial purposes but we use it here for identifying COVID-19 related misinformation posted on Twitter. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
This paper expounds the application and exploration of smart tourism in Daqing under the development of big data technology and the situation of COVID-19. Through the association rules mining, we build Daqing's global tourism data platform and realize intelligent tourism. Combined with the epidemic situation, the digital anti epidemic exploration of Daqing cultural and tourism scenic spot is carried out. Under the guidance of association rule mining and learning, the integration support of each association system is carried out to improve the confidence, so as to establish an all-round, integrated and intelligent smart tourism pattern. © 2022 IEEE.
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In recent years, there have been growing expectations for the creation of new businesses and the improvement of the value of existing services by exchanging data in different fields. Data stored in-house within organizations have become a new source of innovation. While there is a high need for the value creation of data, determining the data value is not an easy task, as there is a wide range of factors to be considered, such as data pricing, acquisition cost, usage value, and update frequency. In this study, we observe communication, such as the sharing of know-hows in data exchange and analysis, and discuss the growing process of a community on the data platform. For the experiment, we focused on the data community in the COVID-19 disaster and used a unique dataset from the data platform Kaggle, which is the data analysis competition service. The results suggest that user actions differ in the discussion of the dataset and analysis. Moreover, providing topics, user participation, and activating actions in the early stages after the dataset is released are essential for forming a data community. We argue that the actions on the data analysis, such as comments and votes, are also crucial for fostering a common understanding of the data value. © 2021 IEEE.
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This paper investigates the state of open government data in the Philippines by comparing access to health information during the COVID-19 pandemic with available open data prior to it. It first assesses the availability and demand for government data through Freedom of Information (FOI) requests and data posted in government Open Data platforms. It then compares this with the emerging lessons from the creation of the health data hub during the pandemic. It analyzes it by considering data openness across three dimensions: content, people, and process. The openness of content subscribes to the accepted definition of data being free to access, and free to manipulate. Openness to people refers to who can actively participate and/or collaborate. Openness of the process pertains to whether the processes involved is transparent and whether the process is open to inputs from participants. It considers lessons from the pandemic as a way forward for more systematic data sharing for the whole of government in the future. © 2021 ACM.
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“Without the health of the whole people, there will be no comprehensive well-off society” that has formed a social consensus. The effective interaction between the “COVID-19” prevention and control and the “Healthy China” strategy has become the key to the construction of a community health information management big data platform. The article analyzes through literature, logical analysis, and other research methods in China. Existing are the main problems in community health information management. On the basis, based on the current situation, put forward the idea of constructing big data of health information management, design and plan the overall framework of health information management platform, make full use of and integrate the advantages of technical resources, serve the health of community residents, improve the health awareness of community residents, and protect and satisfy the community. Residents pursue reasonable needs for health and improve the health of community residents. Furthermore, it provides ideas for the construction of a community health information management big data platform and also provides a theoretical basis for the later development and promotion of platform functions. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
There are many outstanding questions about how to control the global COVID-19 pandemic. The information void has been especially stark in the World Health Organization Africa Region, which has low per capita reported cases, low testing rates, low access to therapeutic drugs, and has the longest wait for vaccines. As with all disease, the central challenge in responding to COVID-19 is that it requires integrating complex health systems that incorporate prevention, testing, front line health care, and reliable data to inform policies and their implementation within a relevant timeframe. It requires that the population can rely on the health system, and decision-makers can rely on the data. To understand the process and challenges of such an integrated response in an under-resourced rural African setting, we present the COVID-19 strategy in Ifanadiana District, where a partnership between Malagasy Ministry of Public Health (MoPH) and non-governmental organizations integrates prevention, diagnosis, surveillance, and treatment, in the context of a model health system. These efforts touch every level of the health system in the district-community, primary care centers, hospital-including the establishment of the only RT-PCR lab for SARS-CoV-2 testing outside of the capital. Starting in March of 2021, a second wave of COVID-19 occurred in Madagascar, but there remain fewer cases in Ifanadiana than for many other diseases (e.g., malaria). At the Ifanadiana District Hospital, there have been two deaths that are officially attributed to COVID-19. Here, we describe the main components and challenges of this integrated response, the broad epidemiological contours of the epidemic, and how complex data sources can be developed to address many questions of COVID-19 science. Because of data limitations, it still remains unclear how this epidemic will affect rural areas of Madagascar and other developing countries where health system utilization is relatively low and there is limited capacity to diagnose and treat COVID-19 patients. Widespread population based seroprevalence studies are being implemented in Ifanadiana to inform the COVID-19 response strategy as health systems must simultaneously manage perennial and endemic disease threats.