181 research outputs found
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
An analytical framework to nowcast well-being using mobile phone data
An intriguing open question is whether measurements made on Big Data
recording human activities can yield us high-fidelity proxies of socio-economic
development and well-being. Can we monitor and predict the socio-economic
development of a territory just by observing the behavior of its inhabitants
through the lens of Big Data? In this paper, we design a data-driven analytical
framework that uses mobility measures and social measures extracted from mobile
phone data to estimate indicators for socio-economic development and
well-being. We discover that the diversity of mobility, defined in terms of
entropy of the individual users' trajectories, exhibits (i) significant
correlation with two different socio-economic indicators and (ii) the highest
importance in predictive models built to predict the socio-economic indicators.
Our analytical framework opens an interesting perspective to study human
behavior through the lens of Big Data by means of new statistical indicators
that quantify and possibly "nowcast" the well-being and the socio-economic
development of a territory
Weather effects on mobile social interactions: a case study of mobile phone users in Lisbon, Portugal
The effect of weather on social interactions has been explored through the analysis of a large mobile phone use dataset. Time spent on phone calls, numbers of connected social ties, and tie strength were used as proxies for social interactions; while weather conditions were characterized in terms of temperature, relative humidity, air pressure, and wind speed. Our results are based on the analysis of a full calendar year of data for 22,696 mobile phone users (53.2 million call logs) in Lisbon, Portugal. The results suggest that different weather parameters have correlations to the level and character of social interactions. We found that although weather did not show much influence upon people's average call duration, the likelihood of longer calls was found to increase during periods of colder weather. During periods of weather that were generally considered to be uncomfortable (i.e., very cold/warm, very low/high air pressure, and windy), people were found to be more likely to communicate with fewer social ties. Despite this tendency, we found that people are more likely to maintain their connections with those they have strong ties with much more than those of weak ties. This study sheds new light on the influence of weather conditions on social relationships and how mobile phone data can be used to investigate the influence of environmental factors on social dynamics
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