Google Mobility Data Research Articles

Trust predicts compliance with COVID-19 containment policies: Evidence from ten countries using big data

We use Twitter, Google mobility, and Oxford policy data to study the relationship between trust and compliance over the period March 2020 to January 2021 in ten, mostly European, countries. Trust has been shown to be an important correlate of compliance with COVID-19 containment policies. However, the previous findings depend upon two assumptions: first, that compliance is time invariant, and second, that compliance can be measured using self reports or mobility measures alone. We relax these assumptions by calculating a new time-varying measure of compliance as the association between containment policies and people's mobility behavior. Additionally, we develop measures of trust in others and national institutions by applying emotion analysis to Twitter data. Results from various panel estimation techniques demonstrate that compliance changes over time and that increasing (decreasing) trust in others predicts increasing (decreasing) compliance. This evidence indicates that compliance changes over time, and further confirms the importance of cultivating trust in others.

Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems

ABSTRACT Big mobility data (BMD) have shown many advantages in studying human mobility and evaluating the performance of transportation systems. However, the quality of BMD remains poorly understood. This study evaluates biases in BMD and develops mitigation methods. Using Google and Apple mobility data as examples, this study compares them with benchmark data from governmental agencies. Spatio-temporal discrepancies between BMD and benchmark are observed and their impacts on transportation applications are investigated, emphasizing the urgent need to address these biases to prevent misguided policymaking. This study further proposes and tests a bias mitigation method. It is shown that the mitigated BMD could generate valuable insights into large-scale public transit systems across 100+ US counties, revealing regional disparities of the recovery of transit systems from the COVID-19. This study underscores the importance of caution when using BMD in transportation research and presents effective mitigation strategies that would benefit practitioners.

Tourism Competitiveness and Mobile Data: A Grey Relational Analysis for European Entrepreneurship Pre & During Pandemic

Abstract The tourism country’s competitiveness is important especially when countries strive for bigger market shares as European countries are. The COVID-19 pandemic has had devastating effects on tourism and the entire tourism industry must be rethinking and reshaping given some opportunities and challenges to the entrepreneurs, local communities, local administrations, governments of competitiveness destinations. In this paper we measured if the Google and Apple mobility indices cand predict (or not) the movement of TTCI during pandemic compared with pre-pandemic TTCI ranks by using grey system theory and statistical methods. The aim of the paper is to validate the Grey Relational Analysis (GRA) as identifier of the good predictor (statistically significant for p-value< 0.05) for European tourism competitiveness (measured by TTCI) in particular conditions, especially before and during COVID-19 pandemic time by take into consideration the Google Mobility and Apple Mobility data and their relationship with overnight stays for 11 European countries. To validate the GRA as method for good and accurate predictor for tourism competitiveness for TTCI 2021 (during COVID-19 pandemic time) a GLM – General Linear Model ANOVA with interaction effects and Tukey HSD Post Hoc Multiple Comparisons was applied. Our results validated the powerful of GRA for tourism competitiveness, statistically significant according to GLM with interaction effects, and emphasis (especially for entrepreneurs) that the overnight stays will give the right rank on top of tourism competitiveness, prior to other ITC support as Google and Apple Mobility indices proved by reflection of overnights on TTCI 2021 – during pandemic.

Integrating Wastewater-Based Epidemiology and Mobility Data to Predict SARS-CoV-2 Cases

Wastewater-based epidemiology has garnered considerable research interest, concerning the COVID-19 pandemic. Restrictive public health interventions and mobility limitations are measures to avert a rising case prevalence. The current study integrates WBE monitoring strategies, Google mobility data, and restriction information to assess the epidemiological development of COVID-19. Various SARIMAX models were employed to predict SARS-CoV-2 cases in Liechtenstein and two Austrian regions. This study analyzes four primary strategies for examining the progression of the pandemic waves, described as follows: 1—a univariate model based on active cases; 2—a multivariate model incorporating active cases and WBE data; 3—a multivariate model considering active cases and mobility data; and 4—a sensitivity analysis of WBE and mobility data incorporating restriction policies. Our key discovery reveals that, while WBE for SARS-CoV-2 holds immense potential for monitoring COVID-19 on a societal level, incorporating the analysis of mobility data and restriction policies enhances the precision of the trained models in predicting the state of public health during the pandemic.

Reduction of the concentration of particulate material at a sampling point in Cusco city at the beginning of the pandemic

The pandemic produced by SARS-CoV-2 generated various impacts on public health, the environment and other anthropogenic activities. The purpose of this study was to evaluate the reduction of air pollution due to PM_{2.5} and PM_{10} particulate matter in Cusco city at the beginning of the pandemic. Social confinement in Peru began on March 16, 2020, until the end of June. These health measures caused strict isolation that resulted in a significant decrease in vehicle flow on the streets and avenues of the city of Cusco. In the first days of May, even during the time of confinement, we managed to measure air quality at a sampling point located on the campus of the Universidad Nacional de San Antonio Abad de Cusco; a reduction in air pollution due to particulate matter was observed. The evaluation was carried out using an high-volume (HiVol) 3000 particulate matter sampler and the mass of particulate matter adhered to the filters was determined by gravimetry. The concentrations of particulate matter PM_{2.5} and PM_{10} obtained pre-pandemic were compared with those recorded at the beginning of the pandemic. The results revealed a significant average reduction in the concentration of PM_{10} and PM_{2.5}, reaching − 57.43% and − 59.52%, respectively, compared to pre-pandemic values. At the same time, its relationship with meteorological parameters and Google mobility data was evaluated and it was concluded that these parameters did not significantly affect the reduction of particulate matter. This study reveals the positive effects of the pandemic in reducing air pollution and the confinement measures had as a secondary effect on the decrease in air pollution in Cusco City.

COVID-19 Excess Deaths in Peru's 25 States in 2020: Nationwide Trends, Confounding Factors, and Correlations With the Extent of Ivermectin Treatment by State.

Introduction In 2020, nations hastened to contain an emerging COVID-19 pandemic by deploying diverse public health approaches, but conclusive appraisals of the efficacy of these approaches are elusive in most cases. One of the medicines deployed, ivermectin (IVM), a macrocyclic lactone having biochemical activity against SARS-CoV-2 through competitive binding to its spike protein, has yielded mixed results in randomized clinical trials (RCTs) for COVID-19 treatments. In Peru, an opportunity to track the efficacy of IVM with a close consideration of confounding factors was provided through data for excess deaths as correlated with IVM use in 2020, under semi-autonomous policies in its 25 states. Methods To evaluate possible IVM treatment effects, excess deaths as determined from Peruvian national health data were analyzed by state for ages ≥60 in Peru's 25 states. These data were compared with monthly summary data for excess deaths in Peru for the period 2020-2021 as published by the WHO in 2022. To identify potential confounding factors, Google mobility data, population densities, SARS-CoV-2 genetic variations, and seropositivity rates were also examined. Results Reductions in excess deaths over a period of 30 days after peak deaths averaged 74% in the 10 states with the most intensive IVM use. As determined across all 25 states, these reductions in excess deaths correlated closely with the extent of IVM use (p<0.002). During four months of IVM use in 2020, before a new president of Peru restricted its use, there was a 14-fold reduction in nationwide excess deaths and then a 13-fold increase in the two months following the restriction of IVM use. Notably, these trends in nationwide excess deaths align with WHO summary data for the same period in Peru. Conclusions The natural experiment that was put into motion with the authorization of IVM use for COVID-19 in Peru in May 2020, as analyzed using data on excess deaths by locality and by state from Peruvian national health sources, resulted in strong evidence for the drug's effectiveness. Several potential confounding factors, including effects of a social isolation mandate imposed in May 2020, variations in the genetic makeup of the SARS-CoV-2 virus, and differences in seropositivity rates and population densities across the 25 states, were considered but did not appear to have significantly influenced these outcomes.

A note on how public transport announcements affect the spread of COVID-19

We investigate the relationship between COVID-19-preventive announcements in public transport hubs and the spread of COVID-19 in Vienna, Austria. Due to its frequent high population densities, the role of public transport is a crucial topic in the field of public health, especially during times of pandemic. Using structural equation modeling, we analyze Google mobility data taken from February to December 2020 and find that changes in the contents of the announcements can influence the pandemic crisis, via their effects on public transport mobility. Interestingly, we find that different announcements affect public transport mobility differently. The impact of these announcements is maintained even after taking the effect of the contemporary government regulations into account. We therefore argue that, in order to fight the spread of the virus, it is important for policymakers to inform the general public about COVID-19-preventive behavior in an appropriate manner. We further suggest that governments take advantage of the large daily flow of people through public transport infrastructure, to disseminate vital information during crises.

Google and Apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach

Research background: The COVID-19 pandemic has caused unprecedented disruptions to the global tourism industry, resulting in significant impacts on both human and economic activities. Travel restrictions, border closures, and quarantine measures have led to a sharp decline in tourism demand, causing businesses to shut down, jobs to be lost, and economies to suffer. Purpose of the article: This study aims to examine the correlation and causal relationship between real-time mobility data and statistical data on tourism, specifically tourism overnights, across eleven European countries during the first 14 months of the pandemic. We analyzed the short longitudinal connections between two dimensions of tourism and related activities. Methods: Our method is to use Google and Apple's observational data to link with tourism statistical data, enabling the development of early predictive models and econometric models for tourism overnights (or other tourism indices). This approach leverages the more timely and more reliable mobility data from Google and Apple, which is published with less delay than tourism statistical data. Findings &amp; value added: Our findings indicate statistically significant correlations between specific mobility dimensions, such as recreation and retail, parks, and tourism statistical data, but poor or insignificant relations with workplace and transit dimensions. We have identified that leisure and recreation have a much stronger influence on tourism than the domestic and routine-named dimensions. Additionally, our neural network analysis revealed that Google Mobility Parks and Google Mobility Retail &amp; Recreation are the best predictors for tourism, while Apple Driving and Apple Walking also show significant correlations with tourism data. The main added value of our research is that it combines observational data with statistical data, demonstrates that Google and Apple location data can be used to model tourism phenomena, and identifies specific methods to determine the extent, direction, and intensity of the relationship between mobility and tourism flows.

Impact of public health policy and mobility change on transmission potential of severe acute respiratory syndrome coronavirus 2 in Rhode Island, March 2020 – November 2021

ABSTRACT To study the SARS-CoV-2 transmission potential in Rhode Island (RI) and its association with policy changes and mobility changes, the time-varying reproduction number, Rt, was estimated. The daily incident case counts (16 March 2020, through 30 November 2021) were bootstrapped within a 15-day sliding window and multiplied by Poisson-distributed multipliers (λ = 4, sensitivity analysis: 11) to generate 1000 estimated infection counts, to which EpiEstim was applied to generate Rt time series. The median Rt percentage change when policies changed was estimated. The time lag correlations were assessed between the 7-day moving average of the relative changes in Google mobility data in the first 90 days, and Rt and estimated infection count, respectively. There were three major pandemic waves in RI in 2020–2021: spring 2020, winter 2020–2021 and fall-winter 2021. The median Rt fluctuated within the range of 0.5–2 from April 2020 to November 2021. Mask mandate (18 April 2020) was associated with a decrease in Rt (−25.99%, 95% CrI: −37.42%, −14.30%). Termination of mask mandates on 6 July 2021 was associated with an increase in Rt (36.74%, 95% CrI: 27.20%, 49.13%). Positive correlations were found between changes in grocery and pharmacy, Rt retail and recreation, transit, and workplace visits, for both Rt and estimated infection count, respectively. Negative correlations were found between changes in residential area visits for both Rt and estimated infection count, respectively. Public health policies enacted in RI were associated with changes in the pandemic trajectory. This ecological study provides further evidence of how non-pharmaceutical interventions and vaccination slowed COVID-19 transmission in RI.

Spatio-temporal analysis of nitrogen dioxide (NO2) from Sentinel-5P imageries using Google Earth Engine changes during the COVID-19 social restriction policy in jakarta

The number of cases due to COVID-19 is increasing, especially in Jakarta, requiring the government to establish a policy of social restrictions. The policy impacting the mobility of people using motor vehicles becomes limited, affecting air quality such as Nitrogen Dioxide (NO2). NO2 concentrations can be determined using the Sentinel-5P satellite using Google Earth Engine. This study analyzed NO2 changes and people's mobility in residential and workplace categories through google mobility data in Jakarta spatially and temporally for January 2020 to December 2021. The Pearson's correlation (R) between NO2 from Sentinel-5P imageries and air quality monitoring stations was 0.426–0.736, indicating a moderate to strong correlation with a p-value <0.05. The decrease in NO2 occurred at the beginning of the Large-Scale Social Restriction (PSBB) from April 2020 to May 2020 and the Implementation of Restrictions on Social Activities (PPKM) policy from July 2021 to August 2021; it was −11.381 ​μmol/m2 and -23.195 ​μmol/m2, with very low and low mobility in the residential and workplace categories. The area that experienced a change in NO2 increase of 1 ​μmol/m2, the highest occurred from May 2021 to June 2021 ​at 641.571 ​km2. Meanwhile, from November 2020 to December 2020, no area experienced an increase of 1 ​μmol/m2. We discovered that there were a number of anomalies when people's mobility in the residential and workplace category was very high, NO2 actually decreased. This anomaly shows that the decrease in NO2 is influenced by decreased mobility and other factors such as precipitation.

Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model

BackgroundForecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy. MethodsCOVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (Reff) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and Reff over the Melbourne Delta outbreak. ResultsCase-only ARIMA model resulted in an R squared (R2) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R2 0.948, RMSE 137.57, and MAPE 21.26. ConclusionMultivariable ARIMA modelling for COVID-19 cases and Reff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.

Built environment factors moderate pandemic fatigue in social distance during the COVID-19 pandemic: A nationwide longitudinal study in the United States

Non-pharmaceutical interventions (NPIs) remain some of the most effective measures for coping with the ever-changing coronavirus disease 2019 (COVID-19) pandemic. Pandemic fatigue, which manifests as the declined willingness to follow the recommended protective behaviors (e.g., keeping social distance policies, wearing masks), has commanded increasing attention from researchers and policymakers after the prolonged NPIs and COVID-19 worldwide. However, long-term changes in pandemic fatigue are not well understood, especially amidst the ever-changing pandemic landscape. Built environment factors have been shown to positively affect mental and physical health, but it is still unclear whether built environments can moderate pandemic fatigue. In this study, we used Google mobility data to investigate longitudinal trends of pandemic fatigue in social distance since the onset of NPIs enforcement in the United States. The results indicated that pandemic fatigue continuously worsened over nearly two years of NPIs implementation, and a sharp increase occurred after the vaccination program began. Additionally, we detected a significant moderation effect of greenspace and urbanicity levels on pandemic fatigue. People living in areas with high levels of greenness or urbanicity experienced lower levels of pandemic fatigue. These findings not only shed new light on the effects of greenness and urbanicity on COVID-19 pandemic fatigue, but also provide evidence for developing more tailored and effective strategies to cope with pandemic fatigue.

A Solution to the Kermack and Mckendrick Integro-Differential Equations Which Accurately Projects Epidemic Dynamics Using Google Mobility Data

A Solution to the Kermack and Mckendrick Integro-Differential Equations Which Accurately Projects Epidemic Dynamics Using Google Mobility Data

COVID-19 Pandemisinin Türkiye'de Troposferik NO2 Üzerine Etkileri

The COVID-19 pandemic has strongly affected the tropospheric NO2 levels due to imposed restrictions on anthropogenic activities. Utilizing space-based estimations of tropospheric NO2, here we examine the relationship of tropospheric NO2 to COVID-19 over Turkey. We have used 2015 - 2019 OMI tropospheric NO2 data as a baseline period and have compared it with 2020. We have found a notable decrease in NO2 in Turkey in April, May, and December while the most significant difference can be observed in the most populated and industrialized cities. The tropospheric NO2 levels returned to nearly a regular pattern in the months that the curfew was partially lifted. We also have used Google Mobility data to explicate the relationship between mobility and the change in NO2 levels for selected cities. Our research corroborates the effects of decreased anthropogenic activity on tropospheric NO2 levels worldwide during the COVID-19 pandemic. However, the distinctive curfew procedures of Turkey revealed a discrete pattern on NO2 levels.

Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility

We explored the viability of using air quality as an alternative to aggregated location data from mobile phones in the two most populated cities in Uganda. We accessed air quality and Google mobility data collected from 15th February 2020 to 10th June 2021 and augmented them with mobility restrictions implemented during the COVID-19 lockdown. We determined whether air quality data depicted similar patterns to mobility data before, during, and after the lockdown and determined associations between air quality and mobility by computing Pearson correlation coefficients (R), conducting multivariable regression with associated confidence intervals (CIs), and visualized the relationships using scatter plots. Residential mobility increased with the stringency of restrictions while both non-residential mobility and air pollution decreased with the stringency of restrictions. In Kampala, PM2.5 was positively correlated with non-residential mobility and negatively correlated with residential mobility. Only correlations between PM2.5 and movement in work and residential places were statistically significant in Wakiso. After controlling for stringency in restrictions, air quality in Kampala was independently correlated with movement in retail and recreation (− 0.55; 95% CI = − 1.01– − 0.10), parks (0.29; 95% CI = 0.03–0.54), transit stations (0.29; 95% CI = 0.16–0.42), work (− 0.25; 95% CI = − 0.43– − 0.08), and residential places (− 1.02; 95% CI = − 1.4– − 0.64). For Wakiso, only the correlation between air quality and residential mobility was statistically significant (− 0.99; 95% CI = − 1.34– − 0.65). These findings suggest that air quality is linked to mobility and thus could be used by public health programs in monitoring movement patterns and the spread of infectious diseases without compromising on individuals’ privacy.

Recursive state and parameter estimation of COVID-19 circulating variants dynamics

COVID-19 pandemic response with non-pharmaceutical interventions is an intrinsic control problem. Governments weigh social distancing policies to avoid overload in the health system without significant economic impact. The mutability of the SARS-CoV-2 virus, vaccination coverage, and mobility restriction measures change epidemic dynamics over time. A model-based control strategy requires reliable predictions to be efficient on a long-term basis. In this paper, a SEIR-based model is proposed considering dynamic feedback estimation. State and parameter estimations are performed on state estimators using augmented states. Three methods were implemented: constrained extended Kalman filter (CEKF), CEKF and smoother (CEKF & S), and moving horizon estimator (MHE). The parameters estimation was based on vaccine efficacy studies regarding transmissibility, severity of the disease, and lethality. Social distancing was assumed as a measured disturbance calculated using Google mobility data. Data from six federative units from Brazil were used to evaluate the proposed strategy. State and parameter estimations were performed from 1 October 2020 to 1 July 2021, during which Zeta and Gamma variants emerged. Simulation results showed that lethality increased between 11 and 30% for Zeta mutations and between 44 and 107% for Gamma mutations. In addition, transmissibility increased between 10 and 37% for the Zeta variant and between 43 and 119% for the Gamma variant. Furthermore, parameter estimation indicated temporal underreporting changes in hospitalized and deceased individuals. Overall, the estimation strategy showed to be suitable for dynamic feedback as simulation results presented an efficient detection and dynamic characterization of circulating variants.

COVID-19 Modeling Outcome versus Reality in Sweden.

It has been very difficult to predict the development of the COVID-19 pandemic based on mathematical models for the spread of infectious diseases, and due to major non-pharmacological interventions (NPIs), it is still unclear to what extent the models would have fit reality in a “do nothing” scenario. To shed light on this question, the case of Sweden during the time frame from autumn 2020 to spring 2021 is particularly interesting, since the NPIs were relatively minor and only marginally updated. We found that state of the art models are significantly overestimating the spread, unless we assume that social interactions significantly decrease continuously throughout the time frame, in a way that does not correlate well with Google-mobility data nor updates to the NPIs or public holidays. This leads to the question of whether modern SEIR-type mathematical models are unsuitable for modeling the spread of SARS-CoV-2 in the human population, or whether some particular feature of SARS-CoV-2 dampened the spread. We show that, by assuming a certain level of pre-immunity to SARS-CoV-2, we obtain an almost perfect data-fit, and discuss what factors could cause pre-immunity in the mathematical models. In this scenario, a form of herd-immunity under the given restrictions was reached twice (first against the Wuhan-strain and then against the alpha-strain), and the ultimate decline in cases was due to depletion of susceptibles rather than the vaccination campaign.

Google Mobility Data as a Predictor for Tourism in Romania during the COVID-19 Pandemic—A Structural Equation Modeling Approach for Big Data

Our exploratory research focuses on the possible relations between tourism and the mobility of people, using short longitudinal data for mobility dimensions during the COVID-19 pandemic. One of these is real-time, exhaustive type data, published by Google, about the mobility of people in six different dimensions, (retail, parks, residential, workplace, grocery, and transit). The aim is to analyze the directional, intensity, causal, and complex interplay between the statistical data of tourism and mobility data for Romanian counties. The main objective is to determine if real-world big data can be linked with tourism arrivals in the first 14 months of the pandemic. We have found, using correlations, factorial analysis (PCA), regression models, and SEM, that there are strong and/or medium relationships between retail and parks and overnights, and weak or no relations between other mobility dimensions (workplace, transit). By applying factorial analysis (PCA), we have regrouped the six Google Mobility dimensions into two new factors that are good predictors for Romanian tourism at the county location. These findings can help provide a better understanding of the relationship between the real movement of people in different urban areas and the tourism phenomenon: the GM parks dimension best predicts tourism indicators (overnights), the GM residential dimension correlates inversely with the tourism indicator, and the rest of the GM indices are generally weak predictors for tourism. A more complex analysis could signal the potential and the character of tourism in different destinations, by territorially and chronologically determining the GM indices that are better linked with the tourism statistical indicators. Further research is required to establish forecasting models using Google Mobility data.

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.

Longitudinal changes in depression and anxiety during COVID-19 crisis in Uruguay.

Longitudinal studies have reported decreased mental health symptoms throughout the COVID-19 crisis, while others have found improvements or no changes across time. However, most research was carried out in developed countries, with a high incidence of COVID-19 and, in several cases, mandatory lockdowns. Considering that Uruguay (a developing country) had a low COVID-19 incidence at the moment of this study and has implemented a mild lockdown, we aimed to evaluate the effect of time and mobility (using Google mobility data) on symptoms of anxiety and depression. A longitudinal panel study with six repeated measures was carried out to evaluate depressive (BDI-II) and anxiety (STAI-S) symptoms during the pandemic. A decline in symptoms of anxiety and depression was found across time. Interestingly, this effect was modulated by age; a greater difference in the symptomatology between age groups was found at the beginning of the measurements than at the end, with the youngest reporting the most severe symptoms. Finally, we found that depressive symptoms decreased as mobility increased. Overall, our findings indicate an improvement in mental health as quarantine passed and mobility increased but following a different pattern depending on age. Monitoring these trajectories is imperative moving forward, especially in vulnerable groups.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12144-022-03460-w.