The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

Background The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. Conclusions The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

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Cervical cancer is the third most common cancer in women worldwide. Human papillomavirus (HPV) has been identified as an etiological factor for cervical cancer. Data on the prevalence and subtype distribution of HPV infection in Jiangxi Province are incomplete. In this study, we investigated HPV subtype distribution and prevalence in Jiangxi Province between August 1, 2010, and December 31, 2015. A total of 71,435 individuals ranging in age from 16 to 77 years were recruited. Cervicovaginal swabs were collected from each participant, and HPV screening was performed. Our results showed that the HPV prevalence was 22.49% in Jiangxi Province. Overall, 14.99% of individuals were positive for a single HPV type, and 7.49% were positive for multiple types. The most frequently detected low-risk genotypes were HPV-6, and high-risk genotypes were HPV-16, -18, -33, -52, and -58. The prevalence and type distribution of HPV infection exhibits regional and age differences; Yingtan had the highest incidence for high-risk HPV infection (32.00%), and peaks in the frequencies of HPV infections were seen for patients under 20 and over 60 years of age. In conclusion, we present data showing that the HPV prevalence varies significantly with age and regions in Jiangxi Province. These results can serve as valuable reference to guide Jiangxi cervical cancer screening and HPV vaccination programs.

The coronavirus disease 2019 has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution of these variables to the disparity in home-dwelling time that reflects the compliance with stay-at-home orders via machine learning approaches. We find statistically significant correlations between the increase in home-dwelling time (r HDT ) and variables that describe economic status in all MSAs, which is further confirmed by the optimized random forest models, because median household income and percentage of high income are the two most important variables in predicting r HDT : The partial dependence between median household income and r HDT reveals that the contribution of income to r HDT is place dependent, nonlinear, and different given varying income intervals. Our study reveals the luxury nature of stay-at-home orders with which lower income groups cannot afford to comply. Such disparity in responses under stay-at-home orders reflects the long-standing social inequity issues in the United States, potentially causing unequal exposure to COVID-19 that disproportionately affects vulnerable populations. We must confront systemic social inequity issues and call for a high-priority assessment of the long-term impact of COVID-19 on geographically and socially disadvantaged groups.

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Effective quantification of visitation is important for understanding many impacts of the COVID-19 pandemic on national parks and other protected areas. In this study, we mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices and released by SafeGraph as part of their Social Distancing Metric dataset. Based on comparisons with visitation statistics released by the U.S. National Park Service, our results confirmed that mobility records from digital devices can effectively capture park visitation patterns but with much finer spatiotemporal granularity. In general, triggers of visitation changes corresponded well with the parks’ management responses to COVID-19, with all six parks showing dramatic decreases in the number of visitors (compared to 2019) beginning in March 2020 and continuing through April and May. As restrictions were eased to promote access to the parks and the benefits associated with outdoor recreation, visitation in 2020 approached or even passed that from 2019 by late summer or early autumn at most of the parks. The results also revealed that parks initially saw the greatest increases in visitation after reopening originating from nearby states, with visitorship coming from a broader range of states as time passed. Our study highlights the capability of mobility data for providing spatiotemporally explicit knowledge of place visitation.

Rapid flood mapping is critical for timely damage assessment and post-event recovery support. Remote sensing provides spatially explicit information for the mapping process, but its real-time imagery is often not available due to bad weather conditions during the event. Using the 2015 South Carolina Flood in downtown Columbia as a case study, this article proposes a novel approach to retrieve near real-time flood probability map by integrating the post-event remote sensing data with the real-time volunteered geographic information (VGI). Relying on each VGI point, an inverse distance weighted height filter was introduced to build a probability index distribution (PID) layer from the high-resolution digital elevation model (DEM) data. For each PID layer, a Gaussian kernel was developed to extract its moisture weight from the normalized difference water index (NDWI) of an EO-1 Advanced Land Imager (ALI) image. Finally, a normalized flood probability map was produced by chaining the moisture weighted PIDs in a Python environment. Results indicate that, by adding the wetness information from post-event satellite observations, the proposed model could provide near real-time flood probability distribution with real-time social media, which is of great importance for emergency responders to quickly identify areas in need of immediate attention.