Dataset Information

Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security.

ABSTRACT: We propose a framework for the systematic analysis of mobile phone data to identify relevant mobility profiles in a population. The proposed framework allows finding distinct human mobility profiles based on the digital trace of mobile phone users characterized by a Matrix of Individual Trajectories (IT-Matrix). This matrix gathers a consistent and regularized description of individual trajectories that enables multi-scale representations along time and space, which can be used to extract aggregated indicators such as a dynamic multi-scale population count. Unsupervised clustering of individual trajectories generates mobility profiles (clusters of similar individual trajectories) which characterize relevant group behaviors preserving optimal aggregation levels for detailed and privacy-secured mobility characterization. The application of the proposed framework is illustrated by analyzing fully anonymized data on human mobility from mobile phones in Senegal at the arrondissement level over a calendar year. The analysis of monthly mobility patterns at the livelihood zone resolution resulted in the discovery and characterization of seasonal mobility profiles related with economic activities, agricultural calendars and rainfalls. The use of these mobility profiles could support the timely identification of mobility changes in vulnerable populations in response to external shocks (such as natural disasters, civil conflicts or sudden increases of food prices) to monitor food security.

SUBMITTER: Zufiria PJ

PROVIDER: S-EPMC5919706 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security.

Zufiria Pedro J PJ Pastor-Escuredo David D Úbeda-Medina Luis L Hernandez-Medina Miguel A MA Barriales-Valbuena Iker I Morales Alfredo J AJ Jacques Damien C DC Nkwambi Wilfred W Diop M Bamba MB Quinn John J Hidalgo-Sanchís Paula P Luengo-Oroz Miguel M

PloS one 20180426 4

We propose a framework for the systematic analysis of mobile phone data to identify relevant mobility profiles in a population. The proposed framework allows finding distinct human mobility profiles based on the digital trace of mobile phone users characterized by a Matrix of Individual Trajectories (IT-Matrix). This matrix gathers a consistent and regularized description of individual trajectories that enables multi-scale representations along time and space, which can be used to extract aggreg ...[more]

PMID: 29698404

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Project description:BackgroundIn early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA.MethodsIn this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (Rt) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of Rt values with mobility proxies.FindingsWe show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [-0·492 to -0·392]), consistent with the emergence of more complex local policies and behaviours, including masking.InterpretationOur study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and Rt. Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health.FundingThere was no funding source for this study.

Project description:BackgroundThe availability of large-scale and fine-grained aggregated mobility data has allowed researchers to observe the dynamics of social distancing behaviors at high spatial and temporal resolutions. Despite the increasing attention paid to this research agenda, limited studies have focused on the demographic factors related to mobility, and the dynamics of social distancing behaviors have not been fully investigated.ObjectiveThis study aims to assist in designing and implementing public health policies by exploring how social distancing behaviors varied among various demographic groups over time.MethodsWe combined several data sources, including mobile tracking mobility data and geographical statistics, to estimate the visiting population of entertainment venues across demographic groups, which can be considered the proxy of social distancing behaviors. Next, we used time series analysis methods to investigate how voluntary and policy-induced social distancing behaviors shifted over time across demographic groups.ResultsOur findings demonstrate distinct patterns of social distancing behaviors and their dynamics across age groups. On the one hand, although entertainment venues' population comprises mainly individuals aged 20-40 years, a more significant proportion of the youth has adopted social distancing behaviors and complied with policy implementations compared to older age groups. From this perspective, the increasing contribution to infections by the youth should be more likely to be attributed to their number rather than their violation of social distancing behaviors. On the other hand, although risk perception and self-restriction recommendations can induce social distancing behaviors, their impact and effectiveness appear to be largely weakened during Japan's second state of emergency.ConclusionsThis study provides a timely reference for policymakers about the current situation on how different demographic groups adopt social distancing behaviors over time. On the one hand, the age-dependent disparity requires more nuanced and targeted mitigation strategies to increase the intention of elderly individuals to adopt mobility restriction behaviors. On the other hand, considering that the effectiveness of policy implementations requesting social distancing behaviors appears to decline over time, in extreme cases, the government should consider imposing stricter social distancing interventions, as they are necessary to promote social distancing behaviors and mitigate the transmission of COVID-19.

Dataset Information

Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security.

Publications

Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security.

Similar Datasets

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