No abstract

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Symbol-based dictionaries of text, images and sound can help individuals with aphasia find the words they need, but are often seen as a last resort because they tend to replace rather than augment the user's natural speech. Through two design investigations, we explore head-worn displays as a means of providing unobtrusive, always-available, and glanceable vocabulary support. The first study used narrative storyboards as a design probe to explore the potential benefits and challenges of a head-worn approach over traditional augmented alternative communication (AAC) tools. The second study then evaluated a proof-ofconcept prototype in both a lab setting with the researcher and in situ with unfamiliar conversation partners at a local market. Findings suggest that a head-worn approach could better allow wearers to maintain focus on the conversation, reduce reliance on the availability of external tools (e.g., paper and pen) or people, and minimize visibility of the support by others. These studies should motivate further investigation of head-worn conversational support.

We analyze data from the 2010 General Social Survey to illuminate the relationship of social capital with pro-environmental behavior, a willingness to make sacrifices for the environment, and participation in organized environmental activism. Three kinds of social capital are examined: relational social capital, generalized trust, and community social capital. Specifically, we find that time spent with neighbors was positively correlated with both environmental lifestyle and willingness to sacrifice variables, whereas time spent with relatives was negatively correlated. Generalized trust was positively correlated with willingness to sacrifice variables, as well. Social evening spent with friends was associated with a single outcome variable: having attended an environmental issue demonstration. These findings are consistent with previous research concerning the influence of community-level dynamics on behavior, and suggest that social capital may be an important, though as of yet not well explored, mechanism for understanding shifts toward pro-environmental behavior.

More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. This work seeks to change this through early detection via machine learning models that predict dyslexia by observing how people interact with a linguistic computer-based game. We designed items of the game taking into account (i) the empirical linguistic analysis of the errors that people with dyslexia make, and (ii) specific cognitive skills related to dyslexia: Language Skills, Working Memory, Executive Functions, and Perceptual Processes.. Using measures derived from the game, we conducted an experiment with 267 children and adults in order to train a statistical model that predicts readers with and without dyslexia using measures derived from the game. The model was trained and evaluated in a 10-fold cross experiment, reaching 84.62% accuracy using the most informative features.