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.

Weather, mobility and the evolution of the Covid-19 pandemic

Purpose The authors investigate the effect of weather and mobility on the spread of the Covid-19 pandemic. Design/methodology/approach The authors first estimate the effective reproduction number (Rt) as a proxy of the spread of the Covid-19 pandemic and then study the relationship between the latter and weather and mobility in a panel data framework. The authors use US daily infections data between February and September of 2020 at the county level. Findings The authors find that lower temperatures are associated with a higher Rt, and this effect is greater at temperatures below 0 degrees C. In addition, mobility reductions related to certain types of locations (retail and recreation, transit stations and workplaces) are effective at reducing Rt, but it is an increase in the time spent in parks that most helps reduce the spread of the pandemic. Originality/value The estimates imply that a 20 degrees C fall in temperature from summer to winter would increase Rt by +0.35, which can be the difference between a well-controlled evolution and explosive behavior of the spread of the virus. Applying these coefficients estimated with US county data to aggregate series from other countries helps explain the resurgence of the pandemic in the Northern Hemisphere during the winter of 2020. The results show that mobility reduction and social distance are best policies to cope with the Covid-19 outbreak. This strong policy lesson will help facing similar outbreaks in the future.

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 impact of COVID-19 on diabetes management for adolescents with T1D and their parents

Introduction: For families with type 1 diabetes (T1D), anxiety from the COVID-19 pandemic may be elevated due to potential for increased vulnerability. Objectives: We aimed to describe the impact of the pandemic on adolescents with T1D and their parents. Methods: In a 2-site (Seattle WA, Houston TX USA) clinical trial of a psychosocial intervention targeting stress/resilience, adolescents 13-18 years old with T1D ≥ 1 year and diabetes distress (PAID-T ≥30) were enrolled with a parent. Using a mixed-methods approach, participants enrolled August 2020-June 2021 completed a survey about the pandemic, including an open-ended question about how COVID impacted T1D management. Survey responses were summarized using frequencies and percentages, and associations between variables were assessed by Chi-squared tests. A1C was extracted from clinical records. Results: Adolescents (n=122) were 56% female, 80% White race, 18% Hispanic, mean A1c = 8.5±2.1%. Parents (n=102) were 79% White, 14% Hispanic, 61% college graduate, 67% reporting annual household income ≥75K USD. 10% of adolescents reported history of COVID-19 infection, 51% had a family member/other important person diagnosed, and 12% had a family member/other important person die from COVID-19 complications. 49% of parents reported loss of job or salary reduction. 29% of adolescents and 33% of parents reported significant struggle to manage T1D during the pandemic (Table 1). Adolescents who reported more difficulty with T1D management were more likely to have A1C ≥ 8%, p<.01. Qualitative themes indicated perceived positive, negative, and neutral effects of the pandemic on: T1D self-care, exercise, food, mental health, telehealth, and motivation. Conclusions: Discussing how the pandemic impacted families' T1D management may be an important focus for clinicians, especially for adolescents with above-target A1C. Strategies to improve resilience for ongoing and future stress may be of value. (Table Presented).