75 research outputs found
Analyzing the Spread of Chagas Disease with Mobile Phone Data
We use mobile phone records for the analysis of mobility patterns and the
detection of possible risk zones of Chagas disease in two Latin American
countries. We show that geolocalized call records are rich in social and
individual information, which can be used to infer whether an individual has
lived in an endemic area. We present two case studies, in Argentina and in
Mexico, using data provided by mobile phone companies from each country. The
risk maps that we generate can be used by health campaign managers to target
specific areas and allocate resources more effectively.Comment: 6 pages, 6 figure
Stochasticity in pandemic spread over the World Airline Network explained by local flight connections
Massive growth in human mobility has dramatically increased the risk and rate
of pandemic spread. Macro-level descriptors of the topology of the World
Airline Network (WAN) explains middle and late stage dynamics of pandemic
spread mediated by this network, but necessarily regard early stage variation
as stochastic. We propose that much of early stage variation can be explained
by appropriately characterizing the local topology surrounding the debut
location of an outbreak. We measure for each airport the expected force of
infection (AEF) which a pandemic originating at that airport would generate. We
observe, for a subset of world airports, the minimum transmission rate at which
a disease becomes pandemically competent at each airport. We also observe, for
a larger subset, the time until a pandemically competent outbreak achieves
pandemic status given its debut location. Observations are generated using a
highly sophisticated metapopulation reaction-diffusion simulator under a
disease model known to well replicate the 2009 influenza pandemic. The
robustness of the AEF measure to model misspecification is examined by
degrading the network model. AEF powerfully explains pandemic risk, showing
correlation of 0.90 to the transmission level needed to give a disease pandemic
competence, and correlation of 0.85 to the delay until an outbreak becomes a
pandemic. The AEF is robust to model misspecification. For 97% of airports,
removing 15% of airports from the model changes their AEF metric by less than
1%. Appropriately summarizing the size, shape, and diversity of an airport's
local neighborhood in the WAN accurately explains much of the macro-level
stochasticity in pandemic outcomes.Comment: article text: 6 pages, 5 figures, 28 reference
Making big data work: smart, sustainable, and safe cities
The goal of the present thematic series is to showcase some of the most relevant contributions submitted to the ‘Telecom Italia Big Data Challenge 2014’ and to provide a discussion venue about recent advances in the appplication of mobile phone and social media data to the study of individual and collective behaviors. Particular attention is devoted to data-driven studies aimed at understanding city dynamics. These studies include: modeling individual and collective traffic patterns and automatically identifying areas with traffic congestion, creating high-resolution population estimates for Milan inhabitants, clustering urban dynamics of migrants and visitors traveling to a city for business or tourism, and investigating the relationship between urban communication and urban happiness
Urban mobility and inequalities. Some perspectives from different disciplines
Urban mobility is one of the key aspects of urban planning and development. It plays an important role in the achievement of a resilient, inclusive and sustainable city. However, the complex interrelations of urban mobility, transportation and other city dimensions implies the need of an interdisciplinary approach to understand and plan it. In this brief paper, we discuss the social aspects of urban mobility and inequality and how it has been addressed in the literature. We also show different ways of gathering data relevant for the understanding of urban mobility, their sizes, scopes, and nature. Finally, we aim to promote an interdisciplinary debate based on our academic literature review about the relationship of urban mobility with social variables such as poverty
Predicting human mobility through the assimilation of social media traces into mobility models
Predicting human mobility flows at different spatial scales is challenged by
the heterogeneity of individual trajectories and the multi-scale nature of
transportation networks. As vast amounts of digital traces of human behaviour
become available, an opportunity arises to improve mobility models by
integrating into them proxy data on mobility collected by a variety of digital
platforms and location-aware services. Here we propose a hybrid model of human
mobility that integrates a large-scale publicly available dataset from a
popular photo-sharing system with the classical gravity model, under a stacked
regression procedure. We validate the performance and generalizability of our
approach using two ground-truth datasets on air travel and daily commuting in
the United States: using two different cross-validation schemes we show that
the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure
A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data
Epidemic outbreaks are an important healthcare challenge, especially in
developing countries where they represent one of the major causes of mortality.
Approaches that can rapidly target subpopulations for surveillance and control
are critical for enhancing containment processes during epidemics.
Using a real-world dataset from Ivory Coast, this work presents an attempt to
unveil the socio-geographical heterogeneity of disease transmission dynamics.
By employing a spatially explicit meta-population epidemic model derived from
mobile phone Call Detail Records (CDRs), we investigate how the differences in
mobility patterns may affect the course of a realistic infectious disease
outbreak. We consider different existing measures of the spatial dimension of
human mobility and interactions, and we analyse their relevance in identifying
the highest risk sub-population of individuals, as the best candidates for
isolation countermeasures. The approaches presented in this paper provide
further evidence that mobile phone data can be effectively exploited to
facilitate our understanding of individuals' spatial behaviour and its
relationship with the risk of infectious diseases' contagion. In particular, we
show that CDRs-based indicators of individuals' spatial activities and
interactions hold promise for gaining insight of contagion heterogeneity and
thus for developing containment strategies to support decision-making during
country-level pandemics
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