Public Opinions on COVID-19 Vaccines-A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method.

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans' sentiment toward vaccines was relatively lower than other races.

COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records.

In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countries publish the number of confirmed cases, dead cases, recovered cases, and locations routinely through various channels and forms. This important data source has enabled researchers worldwide to perform different COVID-19 scientific studies, such as modeling this virus's spreading patterns, developing prevention strategies, and studying the impact of COVID-19 on other aspects of society. However, one major challenge is that there is no standardized, updated, and high-quality data product that covers COVID-19 cases data internationally. This is because different countries may publish their data in unique channels, formats, and time intervals, which hinders researchers from fetching necessary COVID-19 datasets effectively, especially for fine-scale studies. Although existing solutions such as John's Hopkins COVID-19 Dashboard and 1point3acres COVID-19 tracker are widely used, it is difficult for users to access their original dataset and customize those data to meet specific requirements in categories, data structure, and data source selection. To address this challenge, we developed a toolset using cloud-based web scraping to extract, refine, unify, and store COVID-19 cases data at multiple scales for all available countries around the world automatically. The toolset then publishes the data for public access in an effective manner, which could offer users a real time COVID-19 dynamic dataset with a global view. Two case studies are presented about how to utilize the datasets. This toolset can also be easily extended to fulfill other purposes with its open-source nature.

The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective.

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space-time disaggregation.

Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) was first identified in December 2019 in Wuhan, China as an infectious disease, and has quickly resulted in an ongoing pandemic. A data-driven approach was developed to estimate medical resource deficiencies due to medical burdens at county level during the COVID-19 pandemic. The study duration was mainly from February 15, 2020 to May 1, 2020 in the U.S. Multiple data sources were used to extract local population, hospital beds, critical care staff, COVID-19 confirmed case numbers, and hospitalization data at county level. We estimated the average length of stay from hospitalization data at state level, and calculated the hospitalized rate at both state and county level. Then, we developed two medical resource deficiency indices that measured the local medical burden based on the number of accumulated active confirmed cases normalized by local maximum potential medical resources, and the number of hospitalized patients that can be supported per ICU bed per critical care staff, respectively. Data on medical resources, and the two medical resource deficiency indices are illustrated in a dynamic spatiotemporal visualization platform based on ArcGIS Pro Dashboards. Our results provided new insights into the U.S. pandemic preparedness and local dynamics relating to medical burdens in response to the COVID-19 pandemic.

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods.

The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.

Building an Open Resources Repository for COVID-19 Research.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.