Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure

The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.

Serological screening in a large-scale municipal survey in Cascais, Portugal, during the first waves of the COVID-19 pandemic: lessons for future pandemic preparedness efforts

Background

Serological surveys for SARS-CoV-2 were used early in the COVID-19 pandemic to assess epidemiological scenarios. In the municipality of Cascais (Portugal), serological testing combined with a comprehensive socio-demographic, clinical and behavioral questionnaire was offered to residents between May 2020 and beginning of 2021. In this study, we analyze the factors associated with adherence to this municipal initiative, as well as the sociodemographic profile and chronic diseases clinical correlates associated to seropositivity. We aim to contribute with relevant information for future pandemic preparedness efforts.

Methods

This was a cross-sectional study with non-probabilistic sampling. Citizens residing in Cascais Municipality went voluntarily to blood collection centers to participate in the serological survey. The proportion of participants, stratified by socio-demographic variables, was compared to the census proportions to identify the groups with lower levels of adherence to the survey. Univariate and multivariate logistic regression were used to identify socio-demographic, clinical and behavioral factors associated with seropositivity.

Results

From May 2020 to February 2021, 19,608 participants (9.2% of the residents of Cascais) were included in the study. Based on the comparison to census data, groups with lower adherence to this survey were men, the youngest and the oldest age groups, individuals with lower levels of education and unemployed/inactive. Significant predictors of a reactive (positive) serological test were younger age, being employed or a student, and living in larger households. Individuals with chronic diseases generally showed lower seroprevalence.

Conclusion

The groups with low adherence to this voluntary study, as well as the socio-economic contexts identified as more at risk of viral transmission, may be targeted in future pandemic situations. We also found that the individuals with chronic diseases, perceiving higher risk of serious illness, adopted protective behaviors that limited infection rates, revealing that health education on preventive measures was effective for these patients.

Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil

Modeling experts have been continually researching the interplay of human mobility and COVID-19 transmission since the outbreak of the pandemic. They tried to address this problem and support the control of the pandemic spreading at the national or regional levels. However, these modeling approaches had little success in producing empirically verifiable results at the neighborhood level due to a lack of data and limited representation of low spatial scales in the models. To fill this gap, this research aims to present an agent-based model to simulate human mobility choices in the context of COVID-19, based on social activities of individuals in the neighborhood. We apply the VIABLE model to the decision-making process of heterogeneous agents, who populate the system's environment. The agents adapt their mobility and activities autonomously at each iteration to improve their well-being and respond to exposure risks. The study reveals significant temporal variations in mobility choices between the groups of agents with different vulnerability levels under the Covid-19 pandemic. Agents from the same group with similar economic backgrounds tend to select the same mobility patterns and activities leading to segregation at this low scale. We calibrated the model with a focus on Porto Alegre in Brazil.

The effects of COVID-19 on female and male bike sharing users: Insights from Lisbon's GIRA

Women are among the groups most affected by the pandemic as they are more likely to be dependent on public transport (PT), which was heavily restricted during COVID-19. Thus, there is a need to consider transport alternatives such as bike sharing that can ensure their mobility needs. By conducting a survey to the bike sharing system (BSS) of Lisbon, we explored differences in travel behaviour and attitudes between female and male users before and during COVID-19. We found men to have higher bike ownership rates, a higher modal share of personal bicycle regarding commuting, and more likely to use their own bikes if BSS was unavailable. Conversely, women more frequently combined BSS with PT and were more likely to use PT if BSS was unavailable. Moreover, while men were using BSS more frequently than women pre-pandemic, during COVID-19 women are using BSS as frequently as men. Our research provides evidence on the potential role of BSS as a transport alternative during pandemics, inducing women to take up cycling who otherwise would not cycle, therefore, potentially decreasing the current cycling gender gap. Findings suggest that introducing family/friend discounts and promoting BSS for exercising may increase the share of female cyclists.

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis

The objective of this systematic review with PRISMA guidelines is to discover how population movement information has epidemiological implications for the spread of COVID-19. In November 2022, the Web of Science and Scopus databases were searched for relevant reports for the review. The inclusion criteria are: (1) the study uses data from Apple Mobility Trends Reports, (2) the context of the study is about COVID-19 mobility patterns, and (3) the report is published in a peer-reviewed venue in the form of an article or conference paper in English. The review included 35 studies in the period of 2020-2022. The main strategy used for data extraction in this review is a matrix proposal to present each study from a perspective of research objective and outcome, study context, country, time span, and conducted research method. We conclude by pointing out that these data are not often used in studies and it is better to study a single country instead of doing multiple-country research. We propose topic classifications for the context of the studies as transmission rate, transport policy, air quality, re-increased activities, economic activities, and financial markets.

Influence of trip distance and population density on intra-city mobility patterns in Tokyo during COVID-19 pandemic

This study investigates the influence of infection cases of COVID-19 and two non-compulsory lockdowns on human mobility within the Tokyo metropolitan area. Using the data of hourly staying population in each 500m×500m cell and their city-level residency, we show that long-distance trips or trips to crowded places decrease significantly when infection cases increase. The same result holds for the two lockdowns, although the second lockdown was less effective. Hence, Japanese non-compulsory lockdowns influence mobility in a similar way to the increase in infection cases. This means that they are accepted as alarm triggers for people who are at risk of contracting COVID-19.

Simulating the effects of mobility restrictions in the spread of SARS-CoV-2 in metropolitan areas in Portugal

Commuting flows and long-distance travel are important spreading factors of viruses and particularly airborne ones. Therefore, it is relevant to examine the association among diverse mobility scenarios and the spatial dissemination of SARS-CoV-2 cases. We intended to analyze the patterns of virus spreading linked to different mobility scenarios, in order to better comprehend the effect of the lockdown measures, and how such measures can be better informed. We simulated the effects of mobility restrictions in the spread of SARS-CoV-2 amongst the municipalities of two metropolitan areas, Lisbon (LMA) and Porto (PMA). Based on an adapted SEIR (Suscetible-Exposed-Infected-Removed) model, we estimated the number of new daily infections during one year, according to different mobility scenarios: restricted to essential activities, industrial activities, public transport use, and a scenario with unrestricted mobility including all transport modes. The trends of new daily infections were further explored using time-series clustering analysis, using dynamic time warping. Mobility restrictions resulted in lower numbers of new daily infections when compared to the unrestricted mobility scenario, in both metropolitan areas. Between March and September 2020, the official number of new infections followed overall a similar timeline to the one simulated considering only essential activities. At the municipal level, trends differ amongst the two metropolitan areas. The analysis of the effects of mobility in virus spread within different municipalities and regions could help tailoring future strategies and increase the public acceptance of eventual restrictions.

Data-driven analysis of the impact of COVID-19 on Madrid's public transport during each phase of the pandemic

COVID-19 has become a major global issue with large social-economic and health impacts, which led to important changes in people's behavior. One of these changes affected the way people use public transport. In this work we present a data-driven analysis of the impact of COVID-19 on public transport demand in the Community of Madrid, Spain, using data from ticket validations between February and September 2020. This period of time covers all stages of pandemic in Spain, including de-escalation phases. We find that ridership has dramatically decreased by 95% at the pandemic peak, recovering very slowly and reaching only half its pre-pandemic levels at the end of September. We analyze results for different transport modes, ticket types, and groups of users. Our work corroborates that low-income groups are the most reliant on public transportation, thus observing significantly lower decreases in their ridership during pandemic. This paper also shows different average daily patterns of public transit demand during each phase of the pandemic in Madrid. All these findings provide relevant information for transit agencies to design responses to an emergence situation like this pandemic, contributing to extend the global knowledge about COVID-19 impact on transport comparing results with other cities worldwide.

Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data

The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility.

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.

COVID-19 effects on urban driving, walking, and transit usage trends: Evidence from Indian metropolitan cities

The outbreak of the COVID-19 pandemic disrupted all walks of life, including the transportation sector. Fear of the contagion coupled with government regulations to restrict mobility altered the travel behavior of the public. This study proposes integrating freely accessible aggregate mobility datasets published by tech giants Apple and Google, which opens a broader avenue for mobility research in the light of difficult data collection circumstances. A comparative analysis of the changes in usage of different mobility modes during the national lockdown and unlock policy periods across 6 Indian cities (Bangalore, Chennai, Delhi, Hyderabad, Mumbai, and Pune) explain the spatio-temporal differences in mode usages. The study shows a preference for individual travel modes (walking and driving) over public transit. Comparisons with pre-pandemic mode shares present evidence of inertia in the choice of travel modes. Association investigations through generalized linear mixed-effects models identify income, vehicle registrations, and employment rates at the city level to significantly impact the community mobility trends. The methods and interpretations from this study benefit government, planners, and researchers to boost informed policymaking and implementation during a future emergency demanding mobility regulations in the high-density urban conglomerations.

The Impacts of a COVID-19 Related Lockdown (and Reopening Phases) on Time Use and Mobility for Activities in Austria—Results from a Multi-Wave Combined Survey

When activity locations were shut down in the first lockdown to prevent the spread of COVID-19 in Austria, people reduced their trips accordingly. Based on a dataset obtained through a weeklong mobility and activity survey we analyse mobility and time use changes, as well as changes in activity locations and secondary activities. Regression analysis is used to analyse differences in time use changes between socio-demographic groups. We show that trip rates and distances as well as public transport use dropped significantly during the lockdown and did not recover fully in the subsequent opening phase. Former travel time was used for additional leisure, sleep, domestic tasks, and eating in the lockdown, but only the latter two retained their increases in the opening phase. The lockdown resulted in a convergence of time use of socio-demographic groups with formerly different patterns, but the differences reappeared in the opening phase. Our findings are consistent with results from the literature but offer an integrated perspective on mobility and time use not found in either mobility- or time use-focussed studies. We conclude that there is a potential for trip reduction through a shift to virtual performance of activities, but the extent of this shift in post-pandemic times remains unclear.

Human mobility and infection from Covid-19 in the Osaka metropolitan area

Controlling human mobility is thought to be an effective measure to prevent the spread of the COVID-19 pandemic. This study aims to clarify the human mobility types that impacted the number of COVID-19 cases during the medium-term COVID-19 pandemic in the Osaka metropolitan area. The method used in this study was analysis of the statistical relationship between human mobility changes and the total number of COVID-19 cases after two weeks. In conclusion, the results indicate that it is essential to control the human mobility of groceries/pharmacies to between −5 and 5% and that of parks to more than −20%. The most significant finding for urban sustainability is that urban transit was not found to be a source of infection. Hence governments in cities around the world may be able to encourage communities to return to transit mobility, if they are able to follow the kind of hygiene processes conducted in Osaka.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

Characterization of COVID-19's Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain

COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.

Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach

BACKGROUND

From February 2020, both urban and rural Ireland witnessed the rapid proliferation of the COVID-19 disease throughout its counties. During this period, the national COVID-19 responses included stay-at-home directives issued by the state, subject to varying levels of enforcement.

METHODS

In this paper, we present a new method to assess and rank the causes of Ireland COVID-19 deaths as it relates to mobility activities within each county provided by Google while taking into consideration the epidemiological confirmed positive cases reported per county. We used a network structure and rank propagation modelling approach using Personalised PageRank to reveal the importance of each mobility category linked to cases and deaths. Then a novel feature-selection method using relative prominent factors finds important features related to each county's death. Finally, we clustered the counties based on features selected with the network results using a customised network clustering algorithm for the research problem.

FINDINGS

Our analysis reveals that the most important mobility trend categories that exhibit the strongest association to COVID-19 cases and deaths include retail and recreation and workplaces. This is the first time a network structure and rank propagation modelling approach has been used to link COVID-19 data to mobility patterns. The infection determinants landscape illustrated by the network results aligns soundly with county socio-economic and demographic features. The novel feature selection and clustering method presented clusters useful to policymakers, managers of the health sector, politicians and even sociologists. Finally, each county has a different impact on the national total.

Open Business Model of COVID-19 Transformation of an Urban Public Transport System: The Experience of a Large Russian City

Dialectics, or developmental transformation, would eventually cause any system to change. The level and depth of these changes vary and depend on the power of external influence and system reservation mechanisms. The art of managing system processes consists of two main aspects. The first aspect involves the sagacity of managers and predicting general environmental change trends (and their impacts on the managed system). The second involves adjusting to these trends, maximizing possible benefits, and minimizing the negative manifestations of this process. Innovation plays an important role, contributing to system transformations with maximal effect and minimal loss. Public transport systems are important elements in cities, as they provide spatial mobility for at least half of the citizens of a city who cannot use individual transportation. Modern urbanization and peculiarities of the social–economic statuses of many citizens contribute to the fact that organized public transportation is unprofitable. The low solvency of citizens who use public transportation services means that passenger transport systems do not work with enough profitability. As a result, governing institutions often choose to subsidize unprofitable transporter activities, thereby prolonging the functioning of unprofitable routes. This is possible only in conditions of sustainability (in regards to a non-optimal system), when the environment is calm, and its negative impact is low. “Black swans” (according to N. Taleb) are bifurcation factors that break the sustainability of non-optimal system. Urban public transport (UPT) of a large Russian city, Tyumen, experienced it in 2020, in connection with the COVID-19 lockdown. The sharp decrease in population mobility in Tyumen, in 2020–2021, caused the need for a complete transformation of the transport service system. However, managers did not want to fundamentally change a system that consensually suited most counterparties. The search for new balances in a system that demands transformation is one way for sustainable provision. This article looks at the transformation and sustainability of a UPT system in the large Russian city of Tyumen, under conditions affected by the negative impact of COVID-19. Results of a comparative (i.e., pre-crisis (2019) and crisis (2020)) Pareto analysis of the contributions of different UPT routes are presented. Transformation of the structure of the UPT route system can overcome the “crisis” COVID-19 period and minimize its financial-economic costs.

Understanding the Impacts of the COVID-19 Pandemic on Public Transportation Travel Patterns in the City of Lisbon

The ongoing COVID-19 pandemic is creating disruptive changes in urban mobility that may compromise the sustainability of the public transportation system. As a result, worldwide cities face the need to integrate data from different transportation modes to dynamically respond to changing conditions. This article combines statistical views with machine learning advances to comprehensively explore changing urban mobility dynamics within multimodal public transportation systems from user trip records. In particular, we retrieve discriminative traffic patterns with order-preserving coherence to model disruptions to demand expectations across geographies and show their utility to describe changing mobility dynamics with strict guarantees of statistical significance, interpretability and actionability. This methodology is applied to comprehensively trace the changes to the urban mobility patterns in the Lisbon city brought by the current COVID-19 pandemic. To this end, we consider passenger trip data gathered from the three major public transportation modes: subway, bus, and tramways. The gathered results comprehensively reveal novel travel patterns within the city, such as imbalanced demand distribution towards the city peripheries, going far beyond simplistic localized changes to the magnitude of traffic demand. This work offers a novel methodological contribution with a solid statistical ground for the spatiotemporal assessment of actionable mobility changes and provides essential insights for other cities and public transport operators facing mobility challenges alike.

Building back better: The COVID-19 pandemic and transport policy implications for a developing megacity

The COVID-19 pandemic has affected human mobility via lockdowns, social distancing rules, home quarantines, and the full or partial suspension of transportation. Evidence-based policy recommendations are urgently needed to ensure that transport systems have resilience to future pandemic outbreaks, particularly within Global South megacities where demand for public transport is high and reduced access can exacerbate socio-economic inequalities. This study focuses on Metro Manila - a characteristic megacity that experienced one of the most stringent lockdowns worldwide. It analyzes aggregated cell phone and GPS data from Google and Apple that provide a comprehensive representation of mobility behavior before and during the lockdown. While significant decreases are observed for all transport modes, public transport experienced the largest drop (-74.5 %, on average). The study demonstrates that: (i) those most reliant on public transport were disproportionately affected by lockdowns; (ii) public transport was unable to fulfil its role as public service; and, (iii) this drove a paradigm shift towards active mobility. Moving forwards, in the short-term policymakers must promote active mobility and prioritize public transport to reduce unequal access to transport. Longer-term, policymakers must leverage the increased active transport to encourage modal shift via infrastructure investment, and better utilize big data to support decision-making.

COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China

When a public health emergency occurs, a potential sanitation threat will directly change local residents' behavior patterns, especially in high-density urban areas. Their behavior pattern is typically transformed from demand-oriented to security-oriented. This is directly manifested as a differentiation in the population distribution. This study based on a typical area of high-density urban area in central Tianjin, China. We used Baidu heat map (BHM) data to calculate full-day and daytime/nighttime state population aggregation and employed a geographically weighted regression (GWR) model and Moran's I to analyze pre-epidemic/epidemic population aggregation patterns and pre-epidemic/epidemic population flow features. We found that during the COVID-19 epidemic, the population distribution of the study area tended to be homogenous clearly and the density decreased obviously. Compared with the pre-epidemic period: residents' demand for indoor activities increased (average correlation coefficient of the floor area ratio increased by 40.060%); traffic demand decreased (average correlation coefficient of the distance to a main road decreased by 272%); the intensity of the day-and-night population flow declined significantly (its extreme difference decreased by 53.608%); and the large-living-circle pattern of population distribution transformed to multiple small-living circles. This study identified different space utilization mechanisms during the pre-epidemic and epidemic periods. It conducted the minimum living security state of an epidemic-affected city to maintain the operation of a healthy city in the future.