Complete trajectory reconstruction from sparse mobile phone data

Abstract Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique that hinges on tensor factorization as a core method to complete individual CDR-based trajectories. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on an hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.

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Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

Royal Society Open Science ◽

10.1098/rsos.160950 ◽

2017 ◽

Vol 4 (5) ◽

pp. 160950 ◽

Cited By ~ 19

Author(s):

Cecilia Panigutti ◽

Michele Tizzoni ◽

Paolo Bajardi ◽

Zbigniew Smoreda ◽

Vittoria Colizza

Keyword(s):

Infectious Disease ◽

Mobile Phone ◽

Urban Areas ◽

Large Scale ◽

Human Mobility ◽

Mobile Phone Data ◽

Disease Models ◽

Mobility Patterns ◽

Mobility Data ◽

Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

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Mobile phone data reveals the importance of pre-disaster inter-city social ties for recovery after Hurricane Maria

Applied Network Science ◽

10.1007/s41109-019-0221-5 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Takahiro Yabe ◽

Satish V. Ukkusuri ◽

P. Suresh C. Rao

Keyword(s):

Mobile Phone ◽

Large Scale ◽

Human Mobility ◽

Recovery Process ◽

Mobile Phone Data ◽

Mobility Patterns ◽

Social Forms ◽

Recovery Processes ◽

Mobility Data ◽

New Perspective

Abstract Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the recovery process of cities, where inter-city dependencies and intra-city coupling of social and physical systems may affect the outcomes in unforeseen ways. Despite the large implications of understanding the recovery processes of cities after disasters for many domains including critical services, disaster management, and public health, little work have been performed to unravel this complexity. Rather, works are limited to analyzing and modeling cities as independent entities, and have largely neglected the effect that inter-city connectivity may have on the recovery of each city. Large scale mobility data (e.g. mobile phone data, social media data) have enabled us to observe human mobility patterns within and across cities with high spatial and temporal granularity. In this paper, we investigate how inter-city dependencies in both physical as well as social forms contribute to the recovery performances of cities after disasters, through a case study of the population recovery patterns of 78 Puerto Rican counties after Hurricane Maria using mobile phone location data. Various network metrics are used to quantify the types of inter-city dependencies that play an important role for effective post-disaster recovery. We find that inter-city social connectivity, which is measured by pre-disaster mobility patterns, is crucial for quicker recovery after Hurricane Maria. More specifically, counties that had more influx and outflux of people prior to the hurricane, were able to recover faster. Our findings highlight the importance of fostering the social connectivity between cities to prepare effectively for future disasters. This paper introduces a new perspective in the community resilience literature, where we take into account the inter-city dependencies in the recovery process rather than analyzing each community as independent entities.

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Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa

eLife ◽

10.7554/elife.43510 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 27

Author(s):

Sofonias Tessema ◽

Amy Wesolowski ◽

Anna Chen ◽

Maxwell Murphy ◽

Jordan Wilheim ◽

...

Keyword(s):

Mobile Phone ◽

Southern Africa ◽

Human Mobility ◽

Genetic Data ◽

Mobile Phone Data ◽

Malaria Surveillance ◽

Travel History ◽

Cross Border ◽

Mobility Data ◽

Regional Coordination

Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.

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Mapping imported malaria in Bangladesh using parasite genetic and human mobility data

eLife ◽

10.7554/elife.43481 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 22

Author(s):

Hsiao-Han Chang ◽

Amy Wesolowski ◽

Ipsita Sinha ◽

Christopher G Jacob ◽

Ayesha Mahmud ◽

...

Keyword(s):

Mobile Phone ◽

Human Mobility ◽

Genetic Data ◽

Epidemiological Surveillance ◽

Mobile Phone Data ◽

Spatial Spread ◽

Control Programs ◽

Mobility Data ◽

Imported Infections ◽

National Control

For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.

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Radius of Gyration as predictor of COVID-19 deaths trend with three-weeks offset

10.1101/2021.01.30.21250708 ◽

2021 ◽

Author(s):

Alberto Hernando ◽

David Mateo ◽

Jordi Bayer ◽

Ignacio Barrios

Keyword(s):

Mobile Phone ◽

Human Mobility ◽

Critical Value ◽

Mobile Phone Data ◽

Critical Threshold ◽

Radius Of Gyration ◽

Mobility Data ◽

Collateral Effects ◽

Candidate Predictor ◽

Nonpharmaceutical Interventions

AbstractTotal and perimetral lockdowns were the strongest nonpharmaceutical interventions to fight against Covid-19, as well as the with the strongest socioeconomic collateral effects. Lacking a metric to predict the effect of lockdowns in the spreading of COVID-19, authorities and decision-makers opted for preventive measures that showed either too strong or not strong enough after a period of two to three weeks, once data about hospitalizations and deaths was available. We present here the radius of gyration as a candidate predictor of the trend in deaths by COVID-19 with an offset of three weeks. Indeed, the radius of gyration aggregates the most relevant microscopic aspects of human mobility into a macroscopic value, very sensitive to temporary trends and local effects, such as lockdowns and mobility restrictions. We use mobile phone data of more than 13 million users in Spain during a period of one year (from January 6th 2020 to January 10th 2021) to compute the users’ daily radius of gyration and compare the median value of the population with the evolution of COVID-19 deaths: we find that for all weeks where the radius of gyration is above a critical value (70% of its pre-pandemic score) the number of weekly deaths increases three weeks after. The reverse also stands: for all weeks where the radius of gyration is below the critical value, the number of weekly deaths decreased after three weeks. This observation leads to two conclusions: i) the radius of gyration can be used as a predictor of COVID-19-related deaths; and ii) partial mobility restrictions are as effective as a total lockdown as far the radius of gyration is below this critical value.BackgroundAuthorities around the World have used lockdowns and partial mobility restrictions as major nonpharmaceutical interventions to control the expansion of COVID-19. While effective, the efficiency of these measures on the number of COVID-19 cases and deaths is difficult to quantify, severely limiting the feedback that can be used to tune the intensity of these measures. In addition, collateral socioeconomic effects challenge the overall effectiveness of lockdowns in the long term, and the degree by which they are followed can be difficult to estimate. It is desirable to find both a metric to accurately monitor the mobility restrictions and a predictor of their effectiveness.MethodsWe correlate the median of the daily radius of gyration of more than 13M users in Spain during all of 2020 with the evolution of COVID-19 deaths for the same period. Mobility data is obtained from mobile phone metadata from one of the major operators in the country.ResultsThe radius of gyration is a predictor of the trend in the number of COVID-19 deaths with 3 weeks offset. When the radius is above/below a critical threshold (70% of the pre-pandemic score), the number of deaths increases/decreases three weeks later.ConclusionsThe radius of gyration can be used to monitor in real time the effectiveness of the mobility restrictions. The existence of a critical threshold suggest that partial lockdowns can be as efficient as total lockdowns, while reducing their socioeconomic impact. The mechanism behind the critical value is still unknow, and more research is needed.

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Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1522305113 ◽

2016 ◽

Vol 113 (23) ◽

pp. 6421-6426 ◽

Cited By ~ 68

Author(s):

Flavio Finger ◽

Tina Genolet ◽

Lorenzo Mari ◽

Guillaume Constantin de Magny ◽

Noël Magloire Manga ◽

...

Keyword(s):

Mobile Phone ◽

Disease Transmission ◽

Large Scale ◽

Human Mobility ◽

Disease Outbreaks ◽

Mobility Model ◽

Phone Call ◽

Mobile Phone Data ◽

Major Influence ◽

Mass Gatherings

The spatiotemporal evolution of human mobility and the related fluctuations of population density are known to be key drivers of the dynamics of infectious disease outbreaks. These factors are particularly relevant in the case of mass gatherings, which may act as hotspots of disease transmission and spread. Understanding these dynamics, however, is usually limited by the lack of accurate data, especially in developing countries. Mobile phone call data provide a new, first-order source of information that allows the tracking of the evolution of mobility fluxes with high resolution in space and time. Here, we analyze a dataset of mobile phone records of ∼150,000 users in Senegal to extract human mobility fluxes and directly incorporate them into a spatially explicit, dynamic epidemiological framework. Our model, which also takes into account other drivers of disease transmission such as rainfall, is applied to the 2005 cholera outbreak in Senegal, which totaled more than 30,000 reported cases. Our findings highlight the major influence that a mass gathering, which took place during the initial phase of the outbreak, had on the course of the epidemic. Such an effect could not be explained by classic, static approaches describing human mobility. Model results also show how concentrated efforts toward disease control in a transmission hotspot could have an important effect on the large-scale progression of an outbreak.

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Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

10.1101/2021.07.30.21261392 ◽

2021 ◽

Author(s):

Tanjona Ramiadantsoa ◽

C. Jessica E. Metcalf ◽

Antso Hasina Raherinandrasana ◽

Santatra Randrianarisoa ◽

Benjamin L. Rice ◽

...

Keyword(s):

Public Health ◽

Mobile Phone ◽

Gravity Model ◽

Human Mobility ◽

Health Planning ◽

Data Availability ◽

Disease Spread ◽

Mobile Phone Data ◽

Migration Flow ◽

Mobility Data

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

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