Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms.

BACKGROUND: Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns. METHODS: A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States. RESULTS: The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates. CONCLUSIONS: Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

COVID-19 early-alert signals using human behavior alternative data.

Google searches create a window into population-wide thoughts and plans not just of individuals, but populations at large. Since the outbreak of COVID-19 and the non-pharmaceutical interventions introduced to contain it, searches for socially distanced activities have trended. We hypothesize that trends in the volume of search queries related to activities associated with COVID-19 transmission correlate with subsequent COVID-19 caseloads. We present a preliminary analytics framework that examines the relationship between Google search queries and the number of newly confirmed COVID-19 cases in the United States. We designed an experimental tool with search volume indices to track interest in queries related to two themes: isolation and mobility. Our goal was to capture the underlying social dynamics of an unprecedented pandemic using alternative data sources that are new to epidemiology. Our results indicate that the net movement index we defined correlates with COVID-19 weekly new case growth rate with a lag of between 10 and 14 days for the United States at-large, as well as at the state level for 42 out of 50 states with the exception of 8 states (DE, IA, KS, NE, ND, SD, WV, WY) from March to June 2020. In addition, an increasing caseload was seen over the summer in some southern US states. A sharp rise in mobility indices was followed by a sharp increase, respectively, in the case growth data, as seen in our case study of Arizona, California, Florida, and Texas. A sharp decline in mobility indices is often followed by a sharp decline, respectively, in the case growth data, as seen in our case study of Arizona, California, Florida, Texas, and New York. The digital epidemiology framework presented here aims to discover predictors of the pandemic's curve, which could supplement traditional predictive models and inform early warning systems and public health policies.

Challenging Europe: Technology, Environment, and the Quest for Resource Security.

Since the nineteenth century, access to and the development of natural resources became an important element of national and international politics. Resource security emerged as an issue vital to national security; and resource competition and crises gave rise to international tensions as well as to technological innovation and new modes of transnational cooperation.

Exploring Greenland: science and technology in Cold War settings.

This paper explores a vacant spot in the Cold War history of science: the development of research activities in the physical environmental sciences and in nuclear science and technology in Greenland. In the post-war period, scientific exploration of the polar areas became a strategically important element in American and Soviet defence policy. Particularly geophysical fields like meteorology, geology, seismology, oceanography, and others profited greatly from military interest. While Denmark maintained formal sovereignty over Greenland, research activities were strongly dominated by U.S. military interests. This paper sets out to summarize the limited current state of knowledge about activities in the environmental physical sciences in Greenland and their entanglement with military, geopolitical, and colonial interests of both the USA and Denmark. We describe geophysical research in the Cold War in Greenland as a multidimensional colonial endeavour. In a period of decolonization after World War II, Greenland, being a Danish colony, became additionally colonized by the American military. Concurrently, in a period of emerging scientific internationalism, the U.S. military "colonized" geophysical research in the Arctic, which increasingly became subject to military directions, culture, and rules.

[Constructing climate. From classical climatology to modern climate research]. / Klimakonstruktionen. Von der klassischen Klimatologie zur Klimaforschung.

Both climate researchers and historians of climate science have conceived climate as a stable and well defined category. This article argues that such a conception is flawed. In the course of the 19th and 20th century the very concept of climate changed considerably. Scientists came up with different definitions and concepts of climate, which implied different understandings, interests, and research approaches. Understanding climate shifted from a timeless, spatial concept at the end of the 19th century to a spaceless, temporal concept at the end of the 20th. Climatologists in the 19th and early 20th centuries considered climate as a set of atmospheric characteristics associated with specific places or regions. In this context, while the weather was subject to change, climate remained largely stable. Of particular interest was the impact of climate on human beings and the environment. In modern climate research at the close of the 20th century, the concept of climate lost its temporal stability. Instead, climate change has become a core feature of the understanding of climate and a focus of research interests. Climate has also lost its immediate association with specific geographical places and become global. The interest is now focused on the impact of human beings on climate. The paper attempts to investigate these conceptual shifts and their origins and impacts in order to provide a more comprehensive perspective on the history of climate research.

Pathways of maximum likelihood for rare events in nonequilibrium systems: application to nucleation in the presence of shear.

Even in nonequilibrium systems, the mechanism of rare reactive events caused by small random noise is predictable because they occur with high probability via their maximum likelihood path (MLP). Here a geometric characterization of the MLP is given as the curve minimizing a certain functional under suitable constraints. A general purpose algorithm is also proposed to compute the MLP. This algorithm is applied to predict the pathway of transition in a bistable stochastic reaction-diffusion equation in the presence of a shear flow, and to analyze how the shear intensity influences the mechanism and rate of the transition.

The geometric minimum action method for computing minimum energy paths.

An algorithm is proposed to calculate the minimum energy path (MEP). The algorithm is based on a variational formulation in which the MEP is characterized as the curve minimizing a certain functional. The algorithm performs this minimization using a preconditioned steepest-descent scheme with a reparametrization step to enforce a constraint on the curve parametrization.