ABSTRACT
This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing the spread of COVID-19. The main challenge is the scale: nearly six million images are analyzed to observe how people respond to the policy changes. © 1970-2012 IEEE.
ABSTRACT
In recent years, the epidemic of COVID-19 virus has become more and more serious, resulting in more and more people infected by the virus. In view of the transmission and infection characteristics of novel coronavirus and the potential risks of urban epidemic prevention and control, the COVID-19 epidemic prevention and control management system is designed and implemented based on the spring cloud microservice architecture to implement standardized and intelligent management of urban epidemic prevention and control. The mobile terminal is used to collect the nucleic acid detection information, vaccination information and key personnel information of urban residents according to the region, and conduct statistical analysis on all kinds of information to form intelligent visual data for dynamic display, so as to improve the level of epidemic prevention and control management. © 2022 ACM.
ABSTRACT
In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the willingness of many government, researchers, and organizations are led by the initiates of open data to share their data and make them publicly accessible. Healthcare, disease, and epidemiological data, such as privacy-preserving statistics on patients who suffered from epidemic diseases such as Coronavirus disease 2019 (COVID-19), are examples of open big data. Analyzing these open big data can be for social good. For instance, people get a better understanding of the disease by analyzing and mining the disease statistics, which may inspire them to take part in preventing, detecting, controlling and combating the disease. Having a pictorial representation further enhances the understanding of the data and corresponding results for analysis and mining because a picture is worth a thousand words. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. The visualization and visual analytics of sequences of real-life COVID-19 epidemiological data illustrate the ideas. Through our solution, we enable users to visualize the COVID-19 epidemiological data over time. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. The effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data are demonstrated by evaluation of these real-life sequential COVID-19 epidemiological data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The coronavirus disease of 2019 (COVID-19) eruption has perpetrated desolation on educational systems all across the globe. We conducted a fast longitudinal study to find, evaluate, and synthesize research on the repercussions of this epidemic on university sophomores' psychological disorders. We created an interactive simulation to gain a better understanding of the intellectual well-being of university students. Collaborative boards analyze and exhibit actual data as visualizations, statistics, and prose, with a variety of user involvement possibilities. The widgets make it possible to derive meaningful data and present it in a simple and easy-To-understand style. We created an interactive statistics interface to show not only just the latest trends but also crucial metrics and forecasts for the future fortnight. Our panel is simple to use and optimized for effectiveness. It can forcibly temporize values and deploy on any remote server. In this study, we have compared data of pre and during COVID. The data are divided into four categories such as 1) educational impact, 2) family pressure, 3) social and mental health, and 4) stress. Our study found that during prevalent psychological impact is more negative than pre-stage. © 2021 IEEE.
ABSTRACT
At the beginning of 2020, the new coronavirus disease (Covid-19), a deadly viral illness, is declared as a public health emergency situation by WHO. Consequently, it is accepted as pandemic that affected millions of people worldwide. Italy is one of the most affected countries by Covid-19 disease among the world. In this article, our main goal is to investigate the effect of intensity of Covid-19 cases based on the population size and tourism factors in certain regions of Italy by visual data analysis. The regions of Lombardia, Veneto, Campania, Emilia-Romagna, Piemonte are the top five regions covering 58.50% of the total Covid-19 cases diagnosed in Italy. It has been shown by visual data analysis that population and tourism factors play an important role in the spread of Covid-19 cases in these five regions. In addition, a prediction model was created using Bi-LSTM and ARIMA algorithms to forecast the number of Covid-19 cases occurring in these five regions in order to take early action. We can conclude that these northern regions have been affected mostly by Covid-19 and the distribution of the resident population and tourist flow factors affected the number of Covid-19 cases in Italy.
ABSTRACT
A local outbreak of initially unknown cause pneumonia was detected in Wuhan (Hubei, China) in December 2019 and a novel coronavirus, the severe acute respiratory syndrome coronavirus 2, was quickly found to be causing it. Since then, the epidemic has spread to all of China's mainland provinces as well as 58 other countries and territories, with more than 87,137 confirmed cases around the globe, including 79,968 from China, 7169 from other countries as of 1 March 2020, as stated by the World Health Organization in the COVID-19 situation report-41. In response to this current public health emergency, this study done a statistical analysis and visualized reported cases of coronavirus disease 2019 (COVID-19) based on the open data collection provided by Johns Hopkins University. Where the location and number of confirmed infected cases have been shown, there have also been deaths, recovered cases and comparisons of the growth rates between the Globe countries. This was intended to provide researchers, public health officials and the general public with exposure to the epidemic.