Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review

Jones, Kerina; Daniels, Helen; Heys, Sharon; Ford, David

doi:10.2196/mhealth.9974

Cited by 33 publications

(38 citation statements)

References 35 publications

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“…Likewise, reliable self-reporting of symptoms and high ability to identify mosquitoes were other bene ts in mobile apps. On the other hand, mobile phone tracking data in combination with other datasets demonstrated good accuracy to predict areas at risks and outbreaks which is evidenced in previous studies [93,94]. A well-organized coordination of local governments and telecommunication companies is important for enhancing data utilization [95].…”

Section: Bene Ts By Purpose Of Mobile Phonesmentioning

confidence: 86%

The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

Carrillo

Kroeger

Cárdenas

et al. 2020

Preprint

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Background: The rapid expansion of dengue, Zika and chikungunya with large scale outbreaks are an increasing public health concern in many countries. Additionally, the recent coronavirus pandemic urged the need to get connected for fast information transfer and exchange. As response, health programmes have -among other interventions- incorporated digital tools such as mobile phones for supporting the control and prevention of infectious diseases. However, little is known about the benefits of mobile phone technology in terms of input, process and outcome dimensions. The purpose of this scoping review is to analyse the evidence of the use of mobile phones as an intervention tool regarding the performance, acceptance, usability, feasibility, cost and effectiveness in dengue, Zika and chikungunya control programmes.Methods: We conducted a scoping review of studies and reports by systematically searching: i) electronic databases (PubMed, PLOS ONE, PLOS Neglected Tropical Disease, LILACS, WHOLIS, ScienceDirect and Google scholar), ii) grey literature, using Google web and iii) documents in the list of references of the selected papers. Selected studies were categorized using a pre-determined data extraction form. Finally, a narrative summary of the evidence related to general characteristics of available mobile health tools and outcomes was produced.Results: The systematic literature search identified 1289 records, 32 of which met the inclusion criteria and 4 records from the reference lists. A total of 36 studies were included coming from twenty different countries. Five mobile phone services were identified in this review: mobile applications (n = 18), short message services (n=7), camera phone (n = 6), mobile phone tracking data (n = 4), and simple mobile communication (n = 1). Mobile phones were used for surveillance, prevention, diagnosis, and communication demonstrating good performance, acceptance and usability by users, as well as feasibility of mobile phone under real life conditions and effectiveness in terms of contributing to a reduction of vectors/ disease and improving users-oriented behaviour changes. It can be concluded that there are benefits for using mobile phones in the fight against arboviral diseases as well as other epidemic diseases. Further studies particularly on acceptance, cost and effectiveness at scale are recommended.

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Section: Bene Ts By Purpose Of Mobile Phonesmentioning

confidence: 86%

The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

Carrillo

Kroeger

Cárdenas

et al. 2020

Preprint

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“…There are many works that have considered behavior recognition in ubiquitous computing and their relevant subsets, which focus primarily on pervasive datasets stored in BTSs as a subject of these research interests. Mobile phone datasets have gained both in importance and popularity (see the review paper byŞahin and Zhen [1]), both for empirical and theoretical research purposes concerning medicine [2,3], engineering, education, and even ethnic segregation [4]. The CDR datasets are the subject of intensive pre-handling and cleaning to obtain big dataset for further studies that are concerned with behaviors and activities [5].…”

Section: Related Workmentioning

confidence: 99%

Towards Recognising Individual Behaviours from Pervasive Mobile Datasets in Urban Spaces

Klimek

2019

Sustainability

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Mobile phone network data, routinely collected by its providers, possess very valuable encoded information about human behaviors. Intensive tourist activities in urban spaces bring smartness via mobile phone fingerprints into the understanding of an urban ecosystem. Due to the diverse processes that govern mobile communication, mining the geolocations of individuals seems to be non-trivial, tedious, and even irregular, which can lead to an incomplete trajectory. Enriching trajectories with infrastructural facilities is another challenge. We provide a unified approach, comprised of both informal and formal elements, to obtain a common framework, which maps pervasive datasets into a collection of individual patterns in urban spaces, to obtain context-enhanced trajectory reconstructions. Through the algorithmization of the approach, we acquire a study that provides new insights on individual and anonymized tourist behaviors. In order to obtain individual behaviors, it is necessary to carry out an arduous extraction process. We propose a multi-agent system architecture and predefined message streams, which are transported on a message-broker platform. We also propose all of the basic algorithms that compose the prototype of the entire multi-agent system. All algorithms were formally analyzed due to termination and time complexity. System evaluation, together with a few basic experiments, was also carried out. The performance evaluation results authenticate system feasibility, credibility, and vitality. Those factors prove its effectiveness and the possibility to build the target system, whilst supporting every urban ecosystem. The system would also strongly influence municipal services to understand urban context and operate more effectively in order to support tourist activities to become safer and more comfortable.

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“…Applications in diverse domains, including agriculture, transportation, poverty reduction, conflict prevention, disaster response, and humanitarian aid, require knowledge of the distribution of home locations of the population, or of specific demographic sub-groups, for effective public policy interventions [1]- [3]. The conventional approach in these fields is to use census data or data from surveys, such as the American Community Survey (ACS).…”

Section: Introductionmentioning

confidence: 99%

“…New data sources, such as cell phone data records and GPS information, have been considered as alternative sources [1], [2]. However, the use of cell phone data has strict regulatory This material is based upon work supported by the National Science Foundation under Grant No.…”

Section: Introductionmentioning

confidence: 99%

High-resolution home location prediction from tweets using deep learning with dynamic structure

Ghaffari

Srinivasan

Liu

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for effective disaster response and public health intervention, but this is still an open problem. Conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, social media data has been exploited to address this problem by leveraging its large user-base and real-time nature. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. Consequently, much of previous research has aimed only at a coarse spatial resolution, with accuracy being limited for high-resolution methods. In this paper, we develop a deep-learning solution that uses a two-phase dynamic structure to deal with sparse and noisy social media data. In the first phase, high recall is achieved using a random forest, producing more balanced home location candidates. Then two deep neural networks are used to detect home locations with high accuracy. We obtained over 90% accuracy for large subsets on a commonly used dataset. Compared to other high-resolution methods, our approach yields up to 60% error reduction by reducing high-resolution home prediction error from over 21% to less than 8%. Systematic comparisons show that our method gives the highest accuracy both for the entire sample and for subsets. Evaluation on a real-world public health problem further validates the effectiveness of our approach.

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Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review

Cited by 33 publications

References 35 publications

The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

The use of mobile phones for the prevention and control of arboviral diseases: a scoping review

Towards Recognising Individual Behaviours from Pervasive Mobile Datasets in Urban Spaces

High-resolution home location prediction from tweets using deep learning with dynamic structure

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