ABSTRACT
This paper leverages online content to investigate the biggest impact of COVID-19 - remote work, by using China as a primary case study. Telecommuting has become popular since February 2020 primarily due to the pandemic, and people have been slowly returning to their office from May 2020. This study focuses on two time windows in the year 2020 to calculate the growth of different job sectors. Our results indicate the negative impact of teleworking in manufacturing industry, but shows that information technology-related industries are less affected by working from home. This paper also investigates the impact of COVID-19 on the stock market and discussed what plan of action the policy makers should take to provide a good economic environment for the country. In addition to the overall economic situation, we observed how the psychological situation of employees could affect their job performance, indirectly affecting the development of certain industry sectors. Therefore, misinformation in certain Chinese social media channels was also studied in this paper specifically examining the rumors and their latent topics. We believe that our work will initiate a dialogue between scientists, policy makers and government officials to consider the observations highlighted in this paper. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
In response to the pandemic caused by the rapidly spreading COVID-19 virus, several highly effective vaccines have been developed by Pfizer, Moderna, and Janssen. Despite the promising efficacy of those vaccines, there remains the challenge of properly distributing vaccines to those who need it most in the US. of particular concern are individuals who are at higher risk due to underlying medical conditions which have been shown to exacerbate COVID-19 symptoms and at times lead to fatal illnesses. In addition to this, a variety of socioeconomic factors have been linked to increased COVID-19 rates and increased mortality, such as race, age, income, mobility, and education level.This project aims to develop an information system to help advise vaccine distributors and state governments on how to effectively distribute vaccines to prioritize high risk individuals. The information system incorporates state-level data of the population with underlying medical conditions, demographics, overall state income, education level, and state mobility to formulate a mortality index. State-level data on the number of vaccines available and doses already administered are also incorporated into the information system to generate a vaccine index. The mortality and vaccine indices for each state are coupled to generate a vaccine priority ranking which can be used to advise vaccine distribution.The prototype can successfully link the data described above to a map of the US and then color code states according to the vaccine priority ranking. Implementation of this prototype will enable optimal vaccine distribution and reduce instances of severe or fatal COVID-19 illnesses as well as reduce costs associated with oversupply of vaccines in a single region. Future work will focus on improving the granularity of data down to the county-level, as well as increasing the scope of the system to the global scale. Additionally, the team plans to expand the application space of this information system to other diseases. © 2021 IEEE.
ABSTRACT
Federal, state, and local governments have been tracking the spread of the COVID-19 pandemic, an infectious disease caused by a coronavirus. As a result of this pandemic, a consistent stream of health data has been produced that tracks the state of health for the seven billion plus people in the world each day, much of which has been publicly released. This open-source data can be used to analyze, visualize, and explain the spread of COVID-19. Here, a COVID-19 information system was created with the business intelligence software Tableau utilizing big data analysis techniques to contribute to general knowledge about the global pandemic. At a state level, data was collected from the big data initiative, the Covid Tracking Project. At the county level, if not provided in a downloadable format, the data was transcribed into a comma-separated values (CSV) file format and used for analysis. Data collection, cleaning, merging, and filtering was a time consuming and tedious task. The COVID-19 pandemic and increased amount of data collection has highlighted the need to perform rapid data analysis and visualizations. Tableau was used for the rapid prototyping of information system. The available data was decentralized and showed the lack of consistency and lack of nation-wide standardization in collecting COVID-19 data. Uncertainties in the data could be reduced through emphasizing how not all states release the same information, as well as efforts for transparency with how published statistics are calculated. The work aims to draw attention to the need for standardized data collection and the viability of using software such as Tableau for the creation of rapid visualizations. © 2021 ACM.