Quarantine in Motion: A Graph Learning Framework to Reduce Disease Transmission Without Lockdown

Exposure notification applications are developed to increase the scale and speed of disease contact tracing. Indeed, by taking advantage of Bluetooth technology, they track the infected population's mobility and then inform close contacts to get tested. In this paper, we ask whether these applications can extend from reactive to preemptive risk management tools? To this end, we propose a new framework that utilizes graph neural networks (GNN) and real-world Foursquare mobility data to predict high risk locations on an hourly basis. As a proof of concept, we then simulate a risk-informed Foursquare population of over 36,000 people in Austin TX after the peak of an outbreak. We find that even after 50% of the population has been infected with COVID-19, they can still maintain their mobility, while reducing the new infections by 13%. Consequently, these results are a first step towards achieving what we call Quarantine in Motion. © 2022 IEEE.

Gravitating Toward the Arts During the COVID-19 Pandemic

The COVID-19 pandemic has had a profound impact on our lives with individuals experiencing a wide range of emotions. While much attention has been placed on promoting physical health to control the coronavirus, far less attention has been placed on promoting mental health and well-being. There are well-known reports of individuals gravitating toward the arts during times of extreme stress: art is made in prisons;novels are written secretly in police states. In this study we examined whether individuals turned to the arts to cope with the COVID-19 pandemic, how everyday artistic activities helped them regulate their emotions, and whether certain personality traits predicted the use of these strategies. We administered a survey to 486 participants (M-age = 35.7;SDage = 12.3;Range(age) = 18 to 79), asking them about the frequency of artistic activities they engaged in, the artistic activity they felt was the most helpful for coping with the pandemic, and how they used that activity to regulate their emotions. Four findings emerged: (a) participants reported engaging in artistic activities more often during than before the pandemic;(b) listening to music was the most helpful activity, followed by reading, and then the visual arts;(c) artistic activities regulated emotions most commonly by providing a means of escape;and (d) the personality trait of openness to experience predicted use of the approach and self-development emotion regulation strategies. These findings demonstrate that we gravitated toward the arts during the COVID-19 pandemic and that we did so because these activities provided a form of escape.

Pruning digital contact networks for meso-scale epidemic surveillance using foursquare data

With the recent advances in human sensing, the push to integrate human mobility tracking with epidemic modeling highlights the lack of groundwork at the mesoscale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks in the past, existing approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network science based method to first build and then prune the dynamic contact networks for recurring interactions;these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquare's Points of Interest (POI) smart phone geolocation data from over 1.3 million devices to better approximate the COVID-19 infection curves for two major (yet very different) US cities, (i.e., Austin and New York City), while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the mesoscale that can help both policy makers and individuals better understand their estimated state of health and help the pandemic mitigation efforts. © 2021 ACM.

The Moderating Effect of Virtuality on Team Trust and Effectiveness

Research problem: The use of virtual teams (VTs) has been growing steadily since the late 1990s. However, there is disagreement on how the virtuality of a team impacts the relationship of trust and team effectiveness. Some studies have suggested that the operationalization of the virtuality has been simplistic, with most researchers treating virtuality as a binary state. Recently, some researchers have sought to explore more complex conceptualizations of virtuality. Research question: How do dimensions of virtuality impact the relationship between intrateam trust and team effectiveness for virtual teams?Literature review: Researchers have been studying the dynamics of VTs for more than 20 years;however, the prevailing measure of virtuality is unidimensional. Following others, we employ a multidimensional measure of virtuality to model three interactions with the relationship between intrateam trust and team effectiveness. By reviewing relevant team effectiveness, intrateam trust, and virtuality literature, we build a model of team effectiveness based on three dimensions of virtuality. Methodology: A total of 230 subjects on 73 project teams were asked to record their interactions while working on a complex case assignment, allowing us to measure the three dimensions of virtuality. Results: Findings indicate that although Distance Virtuality and Member Virtuality moderate the relationship between intrateam trust and effectiveness, Time Worked Virtually does not, supporting the proposition that virtuality is a multidimensional construct. Conclusion: Differential findings support the multidimensional conceptualization of virtuality. We discuss several implications of our findings for virtual team managers, while paying attention to recent changes in team composition resulting from shelter-in-place orders associated with the COVID-19 pandemic. IEEE