ABSTRACT
In this study, our goal is to identify potentially vulnerable communities that could be subject to ongoing or compounding impacts from the pandemic and/or that may experience a slower recovery due to sociodemographic factors. For this purpose, we compiled information from multiple databases related to sociodemographic and health variables. We used a ranking-based method to integrate them and develop new combined indices. We also investigated a time-dependent correlation between vulnerability components and COVID-19 statistics to understand their time-dependent relationship. We ultimately developed pandemic vulnerability indices by combining CDC's social vulnerability index, our newly developed composite health vulnerability index, and COVID-19 impact indices. We also considered additional assessments include expected annual loss due to natural hazards and community resilience. Potential hot spots (at the county level) were identified throughout the United States, and some general trends were noted. Counties with high COVID-19 impact indices and higher values of the pandemic vulnerability indices were primarily located in the southern United States or coastal areas in the Eastern and Southwestern United States at the beginning of the COVID-19 pandemic. Over time, the computed pandemic vulnerability indices shifted to higher values for counties in the southern and north-central United States, while values calculated for the northwestern and northeastern communities tended to decrease.
ABSTRACT
In this study, our goal is to identify potentially vulnerable communities that could be subject to ongoing or compounding impacts from the pandemic and/or that may experience a slower recovery due to sociodemographic factors. For this purpose, we compiled information from multiple databases related to sociodemographic and health variables. We used a ranking-based method to integrate them and develop new combined indices. We also investigated a time-dependent correlation between vulnerability components and COVID-19 statistics to understand their time-dependent relationship. We ultimately developed pandemic vulnerability indices by combining CDC's social vulnerability index, our newly developed composite health vulnerability index, and COVID-19 impact indices. We also considered additional assessments include expected annual loss due to natural hazards and community resilience. Potential hot spots (at the county level) were identified throughout the United States, and some general trends were noted. Counties with high COVID-19 impact indices and higher values of the pandemic vulnerability indices were primarily located in the southern United States or coastal areas in the Eastern and Southwestern United States at the beginning of the COVID-19 pandemic. Over time, the computed pandemic vulnerability indices shifted to higher values for counties in the southern and north-central United States, while values calculated for the northwestern and northeastern communities tended to decrease.
ABSTRACT
Vaccination is the most effective way to provide long-lasting immunity against viral infection; thus, rapid assessment of vaccine acceptance is a pressing challenge for health authorities. Prior studies have applied survey techniques to investigate vaccine acceptance, but these may be slow and expensive. This study investigates 29 million vaccine-related tweets from August 8, 2020 to April 19, 2021 and proposes a social media-based approach that derives a vaccine acceptance index (VAI) to quantify Twitter users' opinions on COVID-19 vaccination. This index is calculated based on opinion classifications identified with the aid of natural language processing techniques and provides a quantitative metric to indicate the level of vaccine acceptance across different geographic scales in the U.S. The VAI is easily calculated from the number of positive and negative Tweets posted by a specific users and groups of users, it can be compiled for regions such a counties or states to provide geospatial information, and it can be tracked over time to assess changes in vaccine acceptance as related to trends in the media and politics. At the national level, it showed that the VAI moved from negative to positive in 2020 and maintained steady after January 2021. Through exploratory analysis of state- and county-level data, reliable assessments of VAI against subsequent vaccination rates could be made for counties with at least 30 users. The paper discusses information characteristics that enable consistent estimation of VAI. The findings support the use of social media to understand opinions and to offer a timely and cost-effective way to assess vaccine acceptance.