Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions.

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

Canadian Adolescents' Internalized Symptoms in Pandemic Times: Association with Sociodemographic Characteristics, Confinement Habits, and Support.

Adolescence is a time of increased risk for developing symptoms of anxiety and depression, especially for girls. The stress and social isolation experienced during confinement add new threats to already vulnerable adolescents' daily lives. This study is aimed at determining which sociodemographic characteristics (age, family composition, achievement), confinement habits (schedule, new hobby, sleep duration, cellphone and computer use, sports, schoolwork), and sources of support (parents and teachers) are associated with more or less internalized symptoms in Canadian adolescents. Differences between boys and girls are also investigated. Between April 8 and 30 (2020) and through an online survey, 895 Canadian adolescents (74% girls) aged between 12 and 17 years (M = 14.7) were recruited. Path analysis was performed to identify significant associations between sociodemographic characteristics, confinement habits, support variables, and internalized symptoms. Independent samples t-tests and invariance tests were conducted to compare boys and girls. Results suggest that certain confinement habits (time spent using cellphones, doing sports and schoolwork, finding a new hobby) and support variables (parents working outside the home) were significantly and negatively associated with internalized symptoms. Regarding the sex differences, girls used their cellphones more and invariance test results showed that all associations between predictors and symptoms were statistically similar for boys and girls. This study's results help understand better adolescents' experience in confinement. It sheds light on the habits likely to characterize those who are less at risk of experiencing distress, making it possible to better support adolescents during this challenging period.

Limited Technology Access Among Residents of Affordable Senior Housing During the COVID-19 Pandemic.

While U.S. adults living in affordable senior housing represent a vulnerable population during the COVID-19 pandemic, affordable housing may provide a foundation for interventions designed to improve technology access to support health. To better understand technology access among residents of affordable senior housing, we surveyed members of a national association of resident service coordinators to assess their experiences working with residents during the pandemic (n = 1,440). While nearly all service coordinators report that most or all residents have reliable phone access, under a quarter report that most or all have reliable internet access; they also report limited access to technology for video calls. Lack of internet access and technology literacy are perceived as barriers to medical visits and food procurement for low-income older adult residents of affordable housing. Policies to expand internet access as well as training and support to enable use of online services are required to overcome these barriers.

Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II.

BACKGROUND: Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals. OBJECTIVE: By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue. METHODS: Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models. RESULTS: Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020. CONCLUSIONS: In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.

Smartphone addiction across the lifetime during Italian lockdown for COVID-19.

BACKGROUND: Smartphone misuse, also known as Nomophobia is the fear of not being able to consult your own mobile phone, of not being connected or traceable. During the Italian lockdown caused by COVID-19, while the use of technology was the fundamental basis of adaptation for smart working, school and professional training, leading to a change in the population's lifestyle, smartphone dependency caused impaired social relationships. To date, the impact of smartphone dependency in men and women is unclear. We conducted this study with the hypothesis that a period of lockdown fosters the growth of a pathological use of the cell phone different in women and men. OBJECTIVE: The purpose of this work is to investigate gender differences in the level of smartphone dependency in teens and adults during the COVID-19 lockdown period. MATERIAL AND METHODS: The NoMobilePhobia-Questionnaire (NMP-Q) was presented online to 1264 participants between the ages of 15 and 67. RESULTS: The results show no significant main effects for the two factors taken into account (Gender and Age of participants). However, the significant interaction shows that female participants reported on average higher scores on NMP-Q than males, [F(4,1253) =7.06 and p<.001, observed power close to 1 (0.99) and effect size = 0.03 (ETA partial squared)] for the younger age group (15-44), while for those over the age of 44, the average highest scores were for male participants. CONCLUSIONS: One of the "positive" aspects of the COVID-19 pandemic is the use of the Internet and smartphones, and our analysis aimed to document the frequency of use in the Italian context with the NMP-Q. However, we can also conclude that this research is relevant because it can give us a glimpse of the relationship between dependency and mental issues. The results reveal the risk in some of the Italian population of developing forms of smartphone dependency, especially in circumstances that prohibit direct social interactions.

Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing.

We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20-40% in the infection rate in Europe and 30-70% in the US.

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic.

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users' visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

Human mobility trends during the early stage of the COVID-19 pandemic in the United States.

In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic.

While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

Cell Phone Activity in Categories of Places and Associations With Growth in Cases of COVID-19 in the US.

Importance: It is unknown how well cell phone location data portray social distancing strategies or if they are associated with the incidence of coronavirus disease 2019 (COVID-19) cases in a particular geographical area. Objective: To determine if cell phone location data are associated with the rate of change in new COVID-19 cases by county across the US. Design, Setting, and Participants: This cohort study incorporated publicly available county-level daily COVID-19 case data from January 22, 2020, to May 11, 2020, and county-level daily cell phone location data made publicly available by Google. It examined the daily cases of COVID-19 per capita and daily estimates of cell phone activity compared with the baseline (where baseline was defined as the median value for that day of the week from a 5-week period between January 3 and February 6, 2020). All days and counties with available data after the initiation of stay-at-home orders for each state were included. Exposures: The primary exposure was cell phone activity compared with baseline for each day and each county in different categories of place. Main Outcomes and Measures: The primary outcome was the percentage change in COVID-19 cases 5 days from the exposure date. Results: Between 949 and 2740 US counties and between 22 124 and 83 745 daily observations were studied depending on the availability of cell phone data for that county and day. Marked changes in cell phone activity occurred around the time stay-at-home orders were issued by various states. Counties with higher per-capita cases (per 100 000 population) showed greater reductions in cell phone activity at the workplace (ß, -0.002; 95% CI, -0.003 to -0.001; P < 0.001), areas classified as retail (ß, -0.008; 95% CI, -0.011 to -0.005; P < 0.001) and grocery stores (ß, -0.006; 95% CI, -0.007 to -0.004; P < 0.001), and transit stations (ß, -0.003, 95% CI, -0.005 to -0.002; P < 0.001), and greater increase in activity at the place of residence (ß, 0.002; 95% CI, 0.001-0.002; P < 0.001). Adjusting for county-level and state-level characteristics, counties with the greatest decline in workplace activity, transit stations, and retail activity and the greatest increases in time spent at residential places had lower percentage growth in cases at 5, 10, and 15 days. For example, counties in the lowest quartile of retail activity had a 45.5% lower growth in cases at 15 days compared with the highest quartile (SD, 37.4%-53.5%; P < .001). Conclusions and Relevance: Our findings support the hypothesis that greater reductions in cell phone activity in the workplace and retail locations, and greater increases in activity at the residence, are associated with lesser growth in COVID-19 cases. These data provide support for the value of monitoring cell phone location data to anticipate future trends of the pandemic.

Reducing Social Isolation of Seniors during COVID-19 through Medical Student Telephone Contact.

Social isolation has been associated with many adverse health outcomes in older adults. We describe a phone call outreach program in which health care professional student volunteers phoned older adults, living in long-term care facilities and the community, at risk of social isolation during the COVID-19 pandemic. Conversation topics were related to coping, including fears or insecurities, isolation, and sources of support; health; and personal topics such as family and friends, hobbies, and life experiences. Student volunteers felt the calls were impactful both for the students and for the seniors, and call recipients expressed appreciation for receiving the calls and for the physicians who referred them for a call. This phone outreach strategy is easily generalizable and can be adopted by medical schools to leverage students to connect to socially isolated seniors in numerous settings.