Quantifying the Causal Effect of Individual Mobility on Health Status in Urban Space

How does individual mobility in the urban environment impact their health status? Previous works have explored the correlation between human mobility behaviour and individual health, yet the study on the underlying causal effect is woefully inadequate. However, the correlation analysis can sometimes be bewildering because of the confounding effects. For example, older people visit park more often but have worse health status than younger people. The common associations with age will lead to a counter-intuitive negative correlation between park visits and health status. Obtaining causal effects from confounded observations remains a challenge. In this paper, we construct a causal framework based on propensity score matching on multi-level treatment to eliminate the bias brought by confounding effects and estimate the total treatment effects of mobility behaviours on health status. We demonstrate that the matching procedure approximates a de-confounded randomized experiment where confounding variables are balanced substantially. The analysis on the directions of estimated causal effects reveals that fewer neighbouring tobacco shops and frequent visits to sports facilities are related with higher risk in health status, which differs from their correlation directions. Physical mobility behaviours and environment features have more significant estimated effects on health status than contextual mobility behaviours. Moreover, we embed our causal analysis framework in health prediction models to filter out features with superficial correlation but insignificant effects that might lead to over-fitting. This strategy achieves better model robustness with more features filtered out than L1-regularization. Our findings shed light on individual healthy lifestyle and mobility-related health policymaking.

A single-agent extension of the SIR model describes the impact of mobility restrictions on the COVID-19 epidemic

Mobility restrictions are successfully used to contain the diffusion of epidemics. In this work we explore their effect on the epidemic growth by investigating an extension of the Susceptible-Infected-Removed (SIR) model in which individual mobility is taken into account. In the model individual agents move on a chessboard with a Lévy walk and, within each square, epidemic spreading follows the standard SIR model. These simple rules allow to reproduce the sub-exponential growth of the epidemic evolution observed during the Covid-19 epidemic waves in several countries and which cannot be captured by the standard SIR model. We show that we can tune the slowing-down of the epidemic spreading by changing the dynamics of the agents from Lévy to Brownian and we investigate how the interplay among different containment strategies mitigate the epidemic spreading. Finally we demonstrate that we can reproduce the epidemic evolution of the first and second COVID-19 waves in Italy using only 3 parameters, i.e , the infection rate, the removing rate, and the mobility in the country. We provide an estimate of the peak reduction due to imposed mobility restrictions, i. e., the so-called flattening the curve effect. Although based on few ingredients, the model captures the kinetic of the epidemic waves, returning mobility values that are consistent with a lock-down intervention during the first wave and milder limitations, associated to a weaker peak reduction, during the second wave.

Varieties of mobility measures: Comparing survey and mobile phone data during the COVID-19 pandemic

Measures to reduce individual mobility are prime governmental non-pharmaceutical interventions to curb infection rates during a pandemic. To evaluate the effectiveness of these efforts scientific research relies on a variety of mobility measures that commonly stem from two main data sources: survey-self-reports and behavioral mobility data from mobile phones. However, little is known about how mobility from survey self-reports relates to popular mobility estimates using GSM and GPS data. Spanning March 2020 until April 2021 this study compares self-reported mobility from a panel survey in Austria to aggregated mobility estimates utilizing (i) GSM data and (ii) Google's Community Mobility Reports. Our analyses show that correlations in mobility changes over time are high, both in general and when comparing different subgroups. Differences emerge if subgroup differences are compared between mobility estimates. Overall, our findings suggest that these mobility measures manage to capture similar latent variables but researchers should be aware of the specific form of mobility different data sources measure.

The impact of road transport emissions on air quality in Brăila, Romania

Road transport, including accessibility and individual mobility is considered unanimously as a fundamental element of contemporary living. The study area is considering Braila County with a total population of around over 305,000. The area it is well served by 6 national roads, 27 county roads and 42 communal roads and contains some of the most heavily trafficked stretches of road in the Romania. The emissions analysed in this study CH4, CO, CO2, N2O, NH3, NOx, PM2.5 and PM10, were collected by the Agency for Environmental Protection Braila during 2015-2019 based on questionnaires according to EMEP/EEA air pollutant emission inventory guidebook. The highest level of pollutant emissions was recorded in 2017, more exactly 191714,5 Megatons. In this article we analysed five categories of pollution sources: Passenger car, Light commercial trucks, Heavy-duty vehicles, Motorcycles and Non - Road vehicles and other mobile equipment. With the exception of CO2, N2O and NH3, pollutant emissions decreased for the eight pollutants analysed.

Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones

Data mining techniques can extract useful activity and travel information from large-scale data sources such as mobile phone geolocation data. This paper aims to explore individual activity-travel patterns from samples of mobile phone users using a two-week geolocation data set from the Paris region in France. After filtering the data set, we propose techniques to identify individual stays and activity places. Typical activity places such as the primary anchor place and the secondary place are detected. The daily timeline (i.e., activity-travel program) is reconstructed with the detected activity places and the trips in-between. Based on user-day timelines, a three-stage clustering method is proposed for mobility pattern analysis. In the method framework, activity types are first identified by clustering analysis. In the second stage, daily mobility patterns are obtained after clustering the daily mobility features. Activity-travel topologies are statistically investigated to support the interpretation of daily mobility patterns. In the last stage, we analyze statistically the individual mobility patterns for all samples over 14 days, measured by the number of days for all kinds of daily mobility patterns. All individual samples are divided into several groups where people have similar travel behaviors. A kmeans++ algorithm is applied to obtain the appropriate number of patterns in each stage. Finally, we interpret the individual mobility patterns with statistical descriptions and reveal home-based differences in spatial distribution for the grouped individuals.

ASSESSMENT OF THE ACCIDENT RATE OF PERSONAL MOBILITY EQUIPMENT IN VARIOUS DRIVING CONDITIONS

В статье проведен анализ аварийности, связанной с использованием средств индивидуальной мобильности в различных условиях движения. За последние несколько лет количество дорожно-транспортных происшествий с участием указанных средств заметно выросло. Причиной этому явился скачок спроса на данные модели как среди молодежи, так и среди населения более старшего возраста в силу их высокой мобильности. In this article, an analysis of the accident rate of individual mobility equipment in various traffic conditions was carried out. Over the past few years, the number of road accidents involving individual vehicles has increased significantly. The reason for this may be, due to their high mobility, a jump in demand for these models among both young people and the more mature population.

Study of the effectiveness of partial quarantines applied to control the spread of the Covid-19 virus

In Chile and in many countries of the world, partial quarantines have been used as part of the strategy to contain and control the Covid-19 virus. However, there is no certainty of its effectiveness and efficiency due to the lack of comparison with similar scenarios. In this work, we formulated a theoretical model of individual mobility, which also incorporates the infection dynamics of Covid-19. The model is based on a cellular automaton, which includes individuals moving through the represented spatial region and interacting according to the dynamics of Covid-19. In addition, we include mobile and partial health barriers, and different mobility regimes. Our results show that, partial quarantines would not be effective in general, to reduce the peak of active individuals infected with the virus, except for some proportions of territorial area involved in the division of the global region. Another interesting result of our research is that the passage restrictions in a sanitary barrier would not be relevant to the impact of the pandemic indicators in a sanitary quarantine regime. A possible explanation for the ineffectiveness of partial quarantines lies in the fact that the sanitary barriers are permeable to infected individuals and therefore when one of these individuals passes, an outbreak occurs in the virus-free zone that is independent of the original one.