472 research outputs found
Exploiting Cellular Data for Disease Containment and Information Campaigns Strategies in Country-Wide Epidemics
Human mobility is one of the key factors at the basis of the spreading of
diseases in a population. Containment strategies are usually devised on
movement scenarios based on coarse-grained assumptions. Mobility phone data
provide a unique opportunity for building models and defining strategies based
on very precise information about the movement of people in a region or in a
country. Another very important aspect is the underlying social structure of a
population, which might play a fundamental role in devising information
campaigns to promote vaccination and preventive measures, especially in
countries with a strong family (or tribal) structure.
In this paper we analyze a large-scale dataset describing the mobility and
the call patterns of a large number of individuals in Ivory Coast. We present a
model that describes how diseases spread across the country by exploiting
mobility patterns of people extracted from the available data. Then, we
simulate several epidemics scenarios and we evaluate mechanisms to contain the
epidemic spreading of diseases, based on the information about people mobility
and social ties, also gathered from the phone call data. More specifically, we
find that restricting mobility does not delay the occurrence of an endemic
state and that an information campaign based on one-to-one phone conversations
among members of social groups might be an effective countermeasure.Comment: 9 pages, 9 figures. Appeared in Proceedings of NetMob 2013. Boston,
MA, USA. May 201
Disease Containment Strategies based on Mobility and Information Dissemination
Human mobility and social structure are at the basis of disease spreading. Disease containment strategies are usually devised from coarse-grained assumptions about human mobility. Cellular networks data, however, provides finer-grained information, not only about how people move, but also about how they communicate. In this paper we analyze the behavior of a large number of individuals in Ivory Coast using cellular network data. We model mobility and communication between individuals by means of an interconnected multiplex structure where each node represents the population in a geographic area (i.e., a sous-pr\ue9fecture, a third-level administrative region). We present a model that describes how diseases circulate around the country as people move between regions. We extend the model with a concurrent process of relevant information spreading. This process corresponds to people disseminating disease prevention information, e.g., hygiene practices, vaccination campaign notices and other, within their social network. Thus, this process interferes with the epidemic. We then evaluate how restricting the mobility or using preventive information spreading process affects the epidemic. We find that restricting mobility does not delay the occurrence of an endemic state and that an information campaign might be an effective countermeasure
Disease containment strategies based on mobility and information dissemination
Human mobility and social structure are at the basis of disease spreading. Disease containment strategies are usually devised from coarse-grained assumptions about human mobility. Cellular networks data, however, provides finer-grained information, not only about how people move, but also about how they communicate. In this paper we analyze the behavior of a large number of individuals in Ivory Coast using cellular network data. We model mobility and communication between individuals by means of an interconnected multiplex structure where each node represents the population in a geographic area (i.e., a sous-préfecture, a third-level administrative region). We present a model that describes how diseases circulate around the country as people move between regions. We extend the model with a concurrent process of relevant information spreading. This process corresponds to people disseminating disease prevention information, e.g., hygiene practices, vaccination campaign notices and other, within their social network. Thus, this process interferes with the epidemic. We then evaluate how restricting the mobility or using preventive information spreading process affects the epidemic. We find that restricting mobility does not delay the occurrence of an endemic state and that an information campaign might be an effective countermeasure
MuxViz: A Tool for Multilayer Analysis and Visualization of Networks
Multilayer relationships among entities and information about entities must
be accompanied by the means to analyze, visualize, and obtain insights from
such data. We present open-source software (muxViz) that contains a collection
of algorithms for the analysis of multilayer networks, which are an important
way to represent a large variety of complex systems throughout science and
engineering. We demonstrate the ability of muxViz to analyze and interactively
visualize multilayer data using empirical genetic, neuronal, and transportation
networks. Our software is available at https://github.com/manlius/muxViz.Comment: 18 pages, 10 figures (text of the accepted manuscript
On the use of human mobility proxy for the modeling of epidemics
Human mobility is a key component of large-scale spatial-transmission models
of infectious diseases. Correctly modeling and quantifying human mobility is
critical for improving epidemic control policies, but may be hindered by
incomplete data in some regions of the world. Here we explore the opportunity
of using proxy data or models for individual mobility to describe commuting
movements and predict the diffusion of infectious disease. We consider three
European countries and the corresponding commuting networks at different
resolution scales obtained from official census surveys, from proxy data for
human mobility extracted from mobile phone call records, and from the radiation
model calibrated with census data. Metapopulation models defined on the three
countries and integrating the different mobility layers are compared in terms
of epidemic observables. We show that commuting networks from mobile phone data
well capture the empirical commuting patterns, accounting for more than 87% of
the total fluxes. The distributions of commuting fluxes per link from both
sources of data - mobile phones and census - are similar and highly correlated,
however a systematic overestimation of commuting traffic in the mobile phone
data is observed. This leads to epidemics that spread faster than on census
commuting networks, however preserving the order of infection of newly infected
locations. Match in the epidemic invasion pattern is sensitive to initial
conditions: the radiation model shows higher accuracy with respect to mobile
phone data when the seed is central in the network, while the mobile phone
proxy performs better for epidemics seeded in peripheral locations. Results
suggest that different proxies can be used to approximate commuting patterns
across different resolution scales in spatial epidemic simulations, in light of
the desired accuracy in the epidemic outcome under study.Comment: Accepted fro publication in PLOS Computational Biology. Abstract
shortened to fit Arxiv limits. 35 pages, 6 figure
Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing?
Many digital solutions mainly involving Bluetooth technology are being
proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19.
Concerns have been raised regarding privacy, consent, uptake required in a
given population, and the degree to which use of CTAs can impact individual
behaviours. However, very few groups have taken a holistic approach and
presented a combined solution. None has presented their CTA in such a way as to
ensure that even the most suggestible member of our community does not become
complacent and assume that CTA operates as an invisible shield, making us and
our families impenetrable or immune to the disease. We propose to build on some
of the digital solutions already under development that, with addition of a
Bayesian model that predicts likelihood for infection supplemented by
traditional symptom and contact tracing, that can enable us to reach 90% of a
population. When combined with an effective communication strategy and social
distancing, we believe solutions like the one proposed here can have a very
beneficial effect on containing the spread of this pandemic
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Digital traces of human mobility and interaction: models and applications
In the last decade digital devices and services have permeated many aspects of everyday life. They generate massive amounts of data that provide insightful information about how people move across geographic areas and how they interact with others. By analysing this detailed information, it is possible to investigate aspects of human mobility and interaction. Therefore, the thesis of this dissertation is that the analysis of mobility and interaction traces generated by digital devices and services, at different timescales and spatial granularity, can be used to gain a better understanding of human behaviour, build new applications and improve existing services. In order to substantiate this statement I develop analytical models and applications supported by three sources of mobility and interaction data: online social networks, mobile phone networks and GPS traces.
First, I present three applications related to data gathered from online social networks, namely the analysis of a global rumour spreading in Twitter, the definition of spatial dissemination measures in a social graph and the analysis of collaboration between developers in GitHub. Then I describe two applications of the analysis of country-wide data of cellular phone networks: the modelling of epidemic containment strategies, with the goal of assessing their efficacy in curbing infectious diseases; the definition of a mobility-based measure of individual risk, which can be used to identify who needs targeted treatment. Finally, I present two applications based on GPS traces: the estimation of trajectories from spatially-coarse temporally-sparse location traces and the analysis of routing behaviour in urban settings
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