- Published on 26 June 2018
Mobile data can be (and has been) used to study a vast number of subjects related to human behavior. One of its potential applications is on epidemics, a complex field that is informed not only by healthcare, but also social interactions and human mobility. In this blog post, Stefania Rubrichi explains the context in which her team used a real mobile phone dataset in an attempt to better understand and tackle the spread of diseases. Their study was just published in the journal EPJ Data Science.
(Guest post by Stefania Rubrichi, originally published on the SpringerOpen blog)
Epidemics represent an important healthcare challenge worldwide. In a world that is so densely populated and more interconnected than ever, it makes increasingly easier for pathogens to propagate. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment and mitigation processes.
The key to effectively control epidemics is understanding their dynamics and anticipating the possible implications. However, modeling the inherent complexity of disease spread process represents an ever-evolving challenge, requiring continuing efforts at several levels and across a broad range of disciplines. In particular, human behavior factors, like mobility and social interactions, are crucial drivers for disease transmissions, as these can substantially alter the probability of encounters, patterns of exposure, and the likelihood of disease propagation. Modern epidemiology has recognized the increasing importance of such factors and, as a consequence, they are now at the centre of disease dynamics and control investigations.
Mobile phone data have recently offered a pervasive and ubiquitous opportunity to sense individual behavior in many aspects of daily life. Mobile phones have been one of the fastest growing technologies over the last decade, reaching urban and rural populations across all socio-economic spectrums, all over the globe. Associated data, so-called call detail records (CDRs), are automatically generated by phones and recorded at large-scale by carriers for billing purposes, providing extremely rich information on individuals’ displacements and communication activities. They have proven to be extremely useful in research studies turning these data into actionable information and identified as a critical element to support global and sustainable development.
Using a real-world dataset from Ivory Coast, our work presents an attempt to unveil the sociogeographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from pseudoanonymized mobile phone CDRs, it investigates how the differences in mobility patterns may affect the course of a hypothetical infectious disease outbreak. It considers different existing measures of the spatial dimension of human mobility and interactions, and analyses their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in our 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, they show that CDRs-based indicators of individuals’ spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing effective mitigation strategies to support decision-making during country-level epidemics.