Google Community Mobility Reports Research Articles

Effects of Face Mask Mandates on COVID-19 Transmission in 51 Countries: Retrospective Event Study.

The question of the utility of face masks in preventing acute respiratory infections has received renewed attention during the COVID-19 pandemic. However, given the inconclusive evidence from existing randomized controlled trials, evidence based on real-world data with high external validity is missing. To add real-world evidence, this study aims to examine whether mask mandates in 51 countries and mask recommendations in 10 countries increased self-reported face mask use and reduced SARS-CoV-2 reproduction numbers and COVID-19 case growth rates. We applied an event study approach to data pooled from four sources: (1) country-level information on self-reported mask use was obtained from the COVID-19 Trends and Impact Survey, (2) data from the Oxford COVID-19 Government Response Tracker provided information on face mask mandates and recommendations and any other nonpharmacological interventions implemented, (3) mobility indicators from Google's Community Mobility Reports were also included, and (4) SARS-CoV-2 reproduction numbers and COVID-19 case growth rates were retrieved from the Our World in Data-COVID-19 data set. Mandates increased mask use by 8.81 percentage points (P=.006) on average, and SARS-CoV-2 reproduction numbers declined on average by -0.31 units (P=.008). Although no significant average effect of mask mandates was observed for growth rates of COVID-19 cases (-0.98 percentage points; P=.56), the results indicate incremental effects on days 26 (-1.76 percentage points; P=.04), 27 (-1.89 percentage points; P=.05), 29 (-1.78 percentage points; P=.04), and 30 (-2.14 percentage points; P=.02) after mandate implementation. For self-reported face mask use and reproduction numbers, incremental effects are seen 6 and 13 days after mandate implementation. Both incremental effects persist for >30 days. Furthermore, mask recommendations increased self-reported mask use on average (5.84 percentage points; P<.001). However, there were no effects of recommendations on SARS-CoV-2 reproduction numbers or COVID-19 case growth rates (-0.06 units; P=.70 and -2.45 percentage points; P=.59). Single incremental effects on self-reported mask use were observed on days 11 (3.96 percentage points; P=.04), 13 (3.77 percentage points; P=.04) and 25 to 27 (4.20 percentage points; P=.048 and 5.91 percentage points; P=.01) after recommendation. Recommendations also affected reproduction numbers on days 0 (-0.07 units; P=.03) and 1 (-0.07 units; P=.03) and between days 21 (-0.09 units; P=.04) and 28 (-0.11 units; P=.05) and case growth rates between days 1 and 4 (-1.60 percentage points; P=.03 and -2.19 percentage points; P=.03) and on day 23 (-2.83 percentage points; P=.05) after publication. Contrary to recommendations, mask mandates can be used as an effective measure to reduce SARS-CoV-2 reproduction numbers. However, mandates alone are not sufficient to reduce growth rates of COVID-19 cases. Our study adds external validity to the existing randomized controlled trials on the effectiveness of face masks to reduce the spread of SARS-CoV-2.

Correlation of public mobility and Covid-19 incidence in Indonesia during six phases of restriction policy implementation

Public mobility is considered one of the factors thought to impact the transmission of SARS-CoV2. The Indonesian government has imposed six mobility restriction policies since the beginning of the COVID-19 pandemic to cope this situation, however there has been no comprehensive evaluation of this program. This study aimed to examine the correlation between mobility and the incidence of COVID-19 during the implementation of this policy in Indonesia. Secondary data on public mobility, acquired through the Google Community Mobility Reports, and data on COVID-19 new cases, recorded on the COVID Task Force website, were utilized from 2 March 2020 to 20 July 2021.The analysis in this study was carried out using the Pearson correlation test with an alternative Spearman correlation test. The result reported a positive correlation between COVID-19 cases and mobility in certain places, such as in Grocery Store and Transit Station (r = 0.75; p-value &lt;0.001; r = 0.62 p-value &lt;0.001) while there was negative correlation in residential area (r= - 0.276 p-value &lt; 0.001). Public mobility is found to be correlated with an increase in COVID-19 cases during the six phases of restriction policy implementation. In conclusion, regulating public mobility is important to minimize the risk of COVID-19 transmission.

Harnessing mobility data to capture changing work from home behaviours between censuses

AbstractThis paper provides an analysis of working from home patterns in England using data from the 2021 Census to understand (1) how patterns of working from home (WFH) in England have shifted since the COVID‐19 pandemic and (2) whether human mobility indicators, specifically Google Community Mobility Reports, provide a reliable proxy for WFH patterns recorded by the 2021 Census, providing a formal evaluation of the reliability of such datasets, whose applications have grown exponentially over the COVID‐19 pandemic. We find that WFH patterns recorded by the 2021 Census were unique compared with previous UK censuses, reflecting an unprecedented increase likely caused by persistent changes to employment during the COVID‐19 pandemic, with a clear social gradient emerging across the country. We also find that Google mobility in ‘Residential’ and ‘Workplace’ settings provides a reliable measurement of the distribution of WFH populations across Local Authorities, with varying uncertainties for mobility indicators collected in different settings. These findings provide insights into the utility of such datasets to support population research in intercensal periods, where shifts may be occurring, but can be difficult to quantify empirically.

Nowcasting tourist nights spent using innovative human mobility data.

The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition.

The use of Google community mobility reports to model residential waste generation behaviors during and after the COVID-19 lockdown

This paper proposed an original analytical approach to quantify waste generation and recycling behaviors in Regina, a Canadian capital city, using Google's Community Mobility Reports (GCMR) mobility data and ANN modeling. Residential waste collection rate (RWCR) in Regina appears to have changed abruptly during the pandemic. Compared to the pre-COVID-19 lockdown period, the correlation between the percent changes in RWCR and GCMR mobility became increasingly stronger and statistically significant during and after COVID-19 lockdown. It appears that the government imposed public health measures improved the consistency of waste generation and recycling behaviors. All 16 models predicting RWCR performed satisfactorily, with R2 near or over 0.70. The univariate Model A along with multivariate Model B and Model G performed better, with MAE of 11.5 to 15.1 tonnes/day and RMSE of 14.5 to 19.7 tonnes/day. The prediction scores of Model A using a Simple Additive Weighting (SAW) based ranking system seems to be negatively influenced by the distribution of the training and testing sets. There is no distinctive trend in the SAW-based prediction scores among the univariate model and the multivariate models. Instead of absolute values, percent changes and SAW relative ranking are adopted, making the proposed approach appropriate for cross-jurisdictional studies.

Checkpoint Travel Numbers as a Proxy Variable in Population-Based Studies During the COVID-19 Pandemic: Validation Study.

The COVID-19 pandemic had wide-ranging systemic impacts, with implications for social and behavioral factors in human health. The pandemic may introduce history bias in population-level research studies of other health topics during the COVID-19 period. We sought to identify and validate an accessible, flexible measure to serve as a covariate in research spanning the COVID-19 pandemic period. Transportation Security Administration checkpoint travel numbers were used to calculate a weekly sum of daily passengers and validated against two measures with strong face validity: (1) a self-reported item on social distancing practices drawn from a continuous tracking survey among a national sample of youths and young adults (15-24 years) in the United States (N=45,080, approximately 280 unique respondents each week); and (2) Google's Community Mobility Reports, which calculate daily values at the national level to represent rates of change in visits and length of stays to public spaces. For the self-reported survey data, an aggregated week-level variable was calculated as the proportion of respondents who did not practice social distancing that week (January 1, 2019, to May 31, 2022). For the community mobility data, a weekly estimate of change was calculated using daily values compared to a 5-week prepandemic baseline period (January 3, 2020, to February 6, 2020). Spearman rank correlation coefficients were calculated for each comparison. Checkpoint travel data ranged from 668,719 travelers in the week of April 8, 2020, to nearly 15.5 million travelers in the week of May 18, 2022. The weekly proportion of survey respondents who did not practice social distancing ranged from 18.1% (n=42; week of April 15, 2020) to 70.9% (n=213; week of May 25, 2022). The measures were strongly correlated from January 2019 to May 2022 (ρ=0.90, P<.001) and March 2020 to May 2022 (ρ=0.87, P<.001). Strong correlations were observed when analyses were restricted to age groups (15-17 years: ρ=0.90; P<.001; 18-20 years: ρ=0.87; P<.001; 21-24 years: ρ=0.88; P<.001), racial or ethnic minorities (ρ=0.86, P<.001), and respondents with lower socioeconomic status (ρ=0.88, P<.001). There were also strong correlations between the weekly change from the baseline period for checkpoint travel data and community mobility data for transit stations (ρ=0.92, P<.001) and retail and recreation (ρ=0.89, P<.001), and moderate significant correlations for grocery and pharmacy (ρ=0.68, P<.001) and parks (ρ=0.62, P<.001). A strong negative correlation was observed for places of residence (ρ=-0.78, P<.001), and a weak but significant positive correlation was found for workplaces (ρ=0.24, P<.001). The Transportation Security Administration's travel checkpoint data provide a publicly available flexible time-varying metric to control for history bias introduced by the pandemic in research studies spanning the COVID-19 period in the United States.

Digital Literacy and Awareness of User Location Privacy: What People in Turkey Know About Google COVID-19 Community Mobility Reports?

At the outbreak of the COVID-19, governments, health organizations and large technology companies were not prepared for the measures to be taken against the disease. Contact tracking was widely carried out using location data to prevent the spread of COVID-19 with the use of technological tools, especially smartphones. In this epidemic, economic difficulties also emerged due to the lockdown imposed by the governments. For this reason, social distancing and contact tracing applications have become widespread in order to prevent the disease as soon as possible. Such strategies negatively affected individuals’ perceptions of privacy, because authorities gave priority to collecting data from individuals in order to prevent the epidemic. On the other hand, non-governmental organizations suggested that “privacy-first” and “decentralized” approaches should be preferred instead of “data-first” and “centralized” approaches. In this direction, Google and Apple companies have developed a decentralized common API to help fight the virus, which also complies with the European Union’s General Data Protection Regulation. And in particular, Google has regularly shared Google Community Mobility Reports (GCMR) publicly by anonymizing the data it collects from Google Maps. Using these reports people and authorities can get movement data about different categories of places such as retail and recreation, supermarkets and pharmacies, parks, public transport, workplaces and residential.Our aim is to find out whether individuals in Turkey are aware that their location and movement data are being tracked by Google Maps and contact tracing apps for preventing the spread of COVID-19. We also examined their motivations for downloading location tracing apps and whether they have been taking necessary steps to protect their privacy. We investigated whether they had concerns about the possible future use of contact tracing data collected by the health authorities in Turkey and other institutions like Google. The scope of the study covers smartphone users. We collected data through an online survey using Google Forms. Our survey consists mostly of narrative questions, where we asked respondents to imagine various scenarios where app manufacturers, mobile phone operators/manufacturers, or the government were using some of their data to study or mitigate the spread of COVID-19. Then, between 12.15.2020/01.02.2021 we distributed the survey link to the participants through various social media networks. We reported the results of the data of 444 people collected anonymously by quantitative analysis methods. As a result of the study, it was determined that the digital literacy levels of the individuals are high due to their high education level. Despite this, it has been revealed that the participants do not read the terms and conditions offered in apps. Individuals who care about privacy expressed their willingness to share their data for the sake of public health. As an answer to the main question of our study, it was concluded that the participants’ knowledge of GCMR was insufficient. As a matter of fact, it was understood that individuals were indecisive and worried about the use of their data by the authorities in the future due to the uncertainty experienced during the pandemic period.

Viral Viruses and Modified Mobility: Cyberspace Disease Salience Predicts Human Movement Patterns

Humans have a motivational system that influences cognition and behavior to minimize the risk of contact with pathogens. This research examines the relationship between cyberspace disease salience and mobility behavior at the macro and micro levels. Across two studies, we predict and find that people adjust their mobility behavior to minimize the risk of close physical contact with strangers when cyberspace disease salience is high (vs. low). In Study 1, we analyze hourly sales data from five grocery stores and find that when cyberspace disease salience is high (vs. low), consumers spend 28% more money on each shopping trip and grocery stores sell 10% more items per hour despite 10% fewer shoppers per hour. Further, in Study 2, we test the generalizability of these results by analyzing the Google Community Mobility Reports. Here we find that high (vs. low) cyberspace disease salience is associated with an overall decrease in mobility in contexts where the risk of close contact with strangers is high—but not low. We discuss these findings in the context of sustainable consumer (mobility) behavior.

Social factors and UFO reports: was the SARS-CoV-2 pandemic associated with an increase in UFO reporting?

The ongoing SARS-Cov-2 pandemic had many drastic effects upon society beyond the illness and death it caused. Pandemic mitigation measures disrupted and altered behaviors related to social mobility, significantly increasing the time spent at home compared to the pre-pandemic period. Further, it was well documented that social anxiety and stress increased at a population level. Early in the pandemic there was speculation in the popular media that reporting of paranormal phenomena (e.g., UFOs, ghosts, etc.) increased due to factors associated with the pandemic. Past research on UFO/UAP reporting has theorized that increases are triggered by social factors, and so the pandemic provided a natural experiment to test these claims. To measure UFO reports we utilized two public databases of UFO reports for sightings in the United States, provided by the National UFO Reporting Center and the Mutual UFO Network. To estimate the impact of the pandemic we utilized two measures, one for social mobility and one for pandemic/disease severity. Google Community Mobility Reports provided a metric of social mobility for people who use Google Maps on their cellular telephone (i.e., amount of time spent at work compared to home), which we aggregated to a state level to estimate time spent at home. Second, we used new weekly SARS-CoV-2 cases and deaths, both absolute counts and per capita, which can be considered to be an indirect measure of anxiety and stress. We find that UFO reports did increase in 2020 compared to 2019 (p&lt;0.001 for both databases); however, the level of UFO reporting had little to no association with the various pandemic-related measures, offering no support for hypothesized social factors that influence reporting. A complicating factor in UFO reporting is the start in 2019 of Starlink satellite launches. These launches include up to 60 small satellites at once, and so are very distinctive and often easily visible. As a result, many people report these as UFOs. We coded and removed these reports from the sighting databases, and the filtered data similarly have no association with the pandemic-related factors. Further, with Starlink reports removed, there was no increase in sightings in 2020 compared to 2019. Our results contribute to an understanding of large-scale factors that impact the reporting of paranormal events, especially timely given the renewed public and government focus on the UFO phenomenon today.

Did the COVID-19 vaccine rollout impact transportation demand? A case study in New York City

IntroductionThis study investigates the influence of vaccination along with other pandemic-related factors on driving, transit, and walking in New York City (NYC). The results of this study help inform policymakers of the weight of their decisions in a pandemic setting as well as factors to consider when modeling transportation during a pandemic. MethodsIn this study, ARIMAX time series analysis was performed on driving, transit, and walking data from Apple Mobility Trends Reports. The data was segmented into two categories “pre-vaccine” and post-vaccine” for both Manhattan and Brooklyn. The independent variables were primarily COVID-19 statistics (vaccination, case counts, deaths, etc.) along with additional predictors aggregated from Google Community Mobility Reports, Google Trends, Citi Bike, National Oceanic and Atmospheric Administration (NOAA), and the Oxford Covid-19 Government Response Tracker (OxCGRT). ResultsVaccination led to increases in driving, transit, and walking in Brooklyn but was not as statistically significant in Manhattan (the only effect being on walking trips). Despite this, vaccination was not the strongest influencer on transportation. The COVID-19 policy score variable had the highest standardized β in nearly every model, indicating that stricter lockdown policies were the main factor discouraging travel. Furthermore, the lifting of these policies contributed to increases in travel numbers more than vaccination. ConclusionsIn the event of future pandemics or health crises, NYC policymakers should be aware that they play a significant role in mitigating infectious diseases. The public is seemingly more responsive to policy than anything else. Similar studies should be conducted in other cities as the public response may vary based on other factors.

Human Mobility Restrictions and COVID-19 Infection Rates: Analysis of Mobility Data and Coronavirus Spread in Poland and Portugal.

This study examines the possibility of correlation between the data on human mobility restrictions and the COVID-19 infection rates in two European countries: Poland and Portugal. The aim of this study is to verify the correlation and causation between mobility changes and the infection spread as well as to investigate the impact of the introduced restrictions on changes in human mobility. The data were obtained from Google Community Mobility Reports, Apple Mobility Trends Reports, and The Humanitarian Data Exchange along with other reports published online. All the data were organized in one dataset, and three groups of variables were distinguished: restrictions, mobility, and intensity of the disease. The causal-comparative research design method is used for this study. The results show that in both countries the state restrictions reduced human mobility, with the strongest impact in places related to retail and recreation, grocery, pharmacy, and transit stations. At the same time, the data show that the increase in restrictions had strong positive correlation with stays in residential places both in Poland and Portugal.

COVID-19 in Africa: Underreporting, demographic effect, chaotic dynamics, and mitigation strategy impact.

The epidemic of COVID-19 has shown different developments in Africa compared to the other continents. Three different approaches were used in this study to analyze this situation. In the first part, basic statistics were performed to estimate the contribution of the elderly people to the total numbers of cases and deaths in comparison to the other continents; Similarly, the health systems capacities were analysed to assess the level of underreporting. In the second part, differential equations were reconstructed from the epidemiological time series of cases and deaths (from the John Hopkins University) to analyse the dynamics of COVID-19 in seventeen countries. In the third part, the time evolution of the contact number was reconstructed since the beginning of the outbreak to investigate the effectiveness of the mitigation strategies. Results were compared to the Oxford stringency index and to the mobility indices of the Google Community Mobility Reports.Compared to Europe, the analyses show that the lower proportion of elderly people in Africa enables to explain the lower total numbers of cases and deaths by a factor of 5.1 on average (from 1.9 to 7.8). It corresponds to a genuine effect. Nevertheless, COVID-19 numbers are effectively largely underestimated in Africa by a factor of 8.5 on average (from 1.7 to 20. and more) due to the weakness of the health systems at country level. Geographically, the models obtained for the dynamics of cases and deaths reveal very diversified dynamics. The dynamics is chaotic in many contexts, including a situation of bistability rarely observed in dynamical systems. Finally, the contact number directly deduced from the epidemiological observations reveals an effective role of the mitigation strategies on the short term. On the long term, control measures have contributed to maintain the epidemic at a low level although the progressive release of the stringency did not produce a clear increase of the contact number. The arrival of the omicron variant is clearly detected and characterised by a quick increase of interpeople contact, for most of the African countries considered in the analysis.

Correlation of Human Mobility in Capitals of Seven European Countries During the COVID-19 Pandemic

We monitored and compared human mobility for six discrete categories during two year the COVID-19 pandemic in seven European countries: Austria, France, Italy, United Kingdom, Serbia, Spain, and Sweden, and their capitals: Vienna, Paris, Rome, London, Belgrade, Madrid and Stockholm. We have chosen countries whose capitals have more than a million inhabitants and which are located in various parts of Europe. We chose Sweden because it had a policy with the mildest restrictions on population movement during the pandemic. The collected data for the time period from February 15, 2020 until February 11, 2022. Using basic statistical methods, we found that there is a high degree of correlation between the data, which represent the mobility of people across the countries and the mobility of people in the capital of all seven observed European countries for six discrete categories. Based on this, it can be concluded that the mobility of people during the COVID-19 pandemic differs a lot from country to country, because the policies of governments in restricting the movement of people in the past two years have also differed significantly. Through this we want to show that data from Google Community Mobility Reports can be combined with many other data from various areas of human life and work and that various statistical processing of these data can be done to show various types of correlations with human mobility during the COVID-19 pandemic and how it affects the lives and economies of people around the world.

Numerical Simulation to Predict COVID-19 Cases in Punjab.

Coronavirus disease 2019 is a novel disease caused by a newly identified virus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). India recorded its first case of COVID-19 on 30 January 2020. This work is an attempt to calculate the number of COVID-19 cases in Punjab by solving a partial differential equation using the modified cubic B-spline function and differential quadrature method. The real data of COVID-19 cases and Google Community Mobility Reports of Punjab districts were used to verify the numerical simulation of the model. The Google mobility data reflect the changes in social behavior in real time and therefore are an important factor in analyzing the spread of COVID-19 and the corresponding precautionary measures. To investigate the cross-border transmission of COVID-19 between the 23 districts of Punjab with an analysis of human activities as a factor, the 23 districts were divided into five regions. This paper is aimed at demonstrating the predictive ability of the model.

Monitoring public engagement with nature using Google Trends

Abstract How humans interact with nature affects the health of both people and ecosystems. Yet, long‐term data on nature engagement are scarce because traditional survey methods are expensive, time consuming and require commitment over multiple years. Digital data sources (e.g. aggregated data from online searches) have major potential as a supplementary source of information and, in the absence of available data, as a proxy for more direct measurement of nature engagement. Using Google Trends, we created a list of refined and relevant search terms relating to diverse outdoor spaces and activities. We then compared trends in Google search volumes in England across both a 1‐year and 10‐year period to those from Google Community Mobility Reports, and from nationally representative survey data (Natural England's People and Nature Survey and the Monitor of Engagement with the Natural Environment). Search, survey and mobility data all support a general increase in public engagement with nature since 2009, and a more substantial increase during, or following, the initial national ‘lockdown’ period of the COVID‐19 pandemic in England. Search volumes increased for many urban and rural outdoor green spaces (e.g. woodlands), blue spaces (e.g. reservoirs), exercise activities (e.g. walking, running and hiking) and explicitly nature‐based activities (e.g. fishing, wild swimming and encouraging wildlife). Overall, volumes of Google searches were more closely related with longer‐term (10‐year) trends from survey data, than shorter‐term changes during the COVID‐19 pandemic. There were statistically significant relationships between search volumes, survey data (self‐reported past behaviour) and mobility data (movement trends) for around half of comparisons. Of these, an average of 13–44% of variation in the data was explained. The findings show that Google Trends provides valuable information about public engagement with nature, which can help to supplement existing survey data by providing new insights about behavioural trends. The paper also provides a proof of concept for using Google Trends to understand changes in public engagement with nature, which could be applied to the many countries that lack long‐term survey monitoring. Read the free Plain Language Summary for this article on the Journal blog.

Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia.

In response to the COVID-19 pandemic, mobile-phone data on population movement became publicly available, including Google Community Mobility Reports (CMR). This study explored the utilization of mobility data to predict COVID-19 dynamics in Jakarta, Indonesia. We acquired aggregated and anonymized mobility data sets from 15 February to 31 December 2020. Three statistical models were explored: Poisson Regression Generalized Linear Model (GLM), Negative Binomial Regression GLM, and Multiple Linear Regression (MLR). Due to multicollinearity, three categories were reduced into one single index using Principal Component Analysis (PCA). Multiple Linear Regression with variable adjustments using PCA was the best-fit model, explaining 52% of COVID-19 cases in Jakarta (R-Square: 0.52; p < 0.05). This study found that different types of mobility were significant predictors for COVID-19 cases and have different levels of impact on COVID-19 dynamics in Jakarta, with the highest observed in “grocery and pharmacy” (4.12%). This study demonstrates the practicality of using CMR data to help policymakers in decision making and policy formulation, especially when there are limited data available, and can be used to improve health system readiness by anticipating case surge, such as in the places with a high potential for transmission risk and during seasonal events.

Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations

The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown measures were used to assess the changes in the mobility patterns. Time-series and statistical analyses of various air pollutants levels (NO2, O3, SO2, PM10, and aerosol optical depth-AOD) obtained from satellite images and ground monitoring stations were used to assess air quality. The levels of pollutants during the initial lockdown (March to June 2020) and the subsequent gradual reopening in 2020 and 2021 were compared with their average levels during 2015–2019. During the lockdown, people's mobility in the workplace, parks, shops and pharmacies, transit stations, and retail and recreation sectors decreased by about 34%–79%. However, the mobility in the residential sector increased by up to 29%. The satellite-based data indicated significant reductions in NO2 (up to 22%), SO2 (up to 17%), and AOD (up to 40%) with small changes in O3 (up to 5%) during the lockdown. Similarly, data from the ground monitoring stations showed significant reductions in NO2 (49% ̶ 57%) and PM10 (19% ̶ 64%); however, the SO2 and O3 levels showed inconsistent trends. The ground and satellite-based air quality levels were positively correlated for NO2, PM10, and AOD. The data also demonstrated significant correlations between the mobility and NO2 and AOD levels during the lockdown and recovery periods. The study documents the impacts of the lockdown on people's mobility and air quality and provides useful data and analyses for researchers, planners, and policymakers relevant to managing risk, mobility, and air quality.

REGIONAL POPULATION MOBILITY AND OUTPATIENT RETINA VISITS IN THE UNITED STATES DURING THE COVID-19 PANDEMIC.

To characterize how community mobility patterns across the United States correlate with local changes in retina visits during the pandemic. Outpatient retina clinic visits were linked to population mobility by combining multiple public data sets, including the Google Community Mobility Reports and data from the Centers for Disease Control and Infection. Percentage change from baseline in daily-average number of retina visits by county and mobility were measured by county. A total of 2,159,689 patient visits were examined across 332 counties. Daily-average retina visits decreased by 7.0%, 19.0%, 5.0%, and 4.0% from Quarter 1 to 4 of 2020. This decrease was negatively correlated with increased incident of COVID-19 deaths for Quarters 1 to 3 (r = -0.13, r = -0.16, and r = -0.15, respectively, P < 0.001) and increased incident cases for Quarters 1 and 2 (r = -0.18, r = -0.13, respectively, P < 0.001). Daily-average retina visits relative to baseline were significantly lower for metropolitan counties in Quarters 1 and 2, compared with urban and rural (P < 0.001). The decline in retina visits had greatest association with decline in workplace visits in Quarters 1 to 3 (r = 0.27, r = 0.09, r = 0.12, respectively, P < 0.001 for all). This study provides insight into how regional mobility patterns may help to explain and predict patient behaviors and retina outpatient visit responses during the COVID-19 pandemic.

Mobility restrictions and mental health among young adults during the COVID-19 pandemic in Ecuador

ObjectiveTo study the association between mobility restriction and mental health outcomes among Ecuadorian young adults. MethodThe present is a cross-sectional study that included a non-probabilistic sample of mostly highly educated young adults. Socio-demographic and mental health data were collected through an online survey, between May and June 2020, when confinement was mandatory in Ecuador. Data on mobility was extracted from Google Community Mobility Reports. Four aspects of the participants’ mental health were evaluated: eating behavior (emotional eating), depression, sleep quality and sense of coherence as a proxy of resilience, using previously validated instruments. Data were analyzed using multiple linear regression models in R. ResultsSample included 8426 young adults, with a mean age of 22.85 (standard deviation: 4.43), most of whom were women (n=5943, 70.53%). During mandatory confinement, in Ecuador mobility in general was reduced by nearly 50% in comparison to January/February, 2020. Less healthy eating behavior, depression, worse sleep quality and lower sense of coherence were associated with higher mobility restriction to workplaces and groceries/pharmacies. Women and youngsters more often showed depression, less healthy eating behavior, worse quality of sleep and lower sense of coherence in comparison with men and older respondents. ConclusionsMobility restrictions during COVID-19 pandemic were associated with worse self-reported mental health in a sample of young highly-educated Ecuadorian adults. Women and youngsters were the most affected. Our findings highlight the need of implementing health promotion measures directed to ameliorate the effects of confinement on mental health, focusing on women and youngsters.

A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach

We investigate the impact of the COVID-19 outbreak on PM2.5 levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM2.5 levels over the contiguous U.S. in March–May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) μg/m3, and 2.04 (1.87) μg/m3, respectively. Results from Google Community Mobility Reports and estimated PM2.5 concentrations show a greater reduction of PM2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM2.5 (i.e., the ratio of vehicular PM2.5 to other sources of PM2.5) emissions and PM2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM2.5 generally experience greater decreases in PM2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March–May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM2.5 emissions, highlighting the great impact of human activity on PM2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO2, SO4, and especially NO2, appear to have had a significantly greater impact on PM2.5 changes during the study period.