Rubrichi, S;
Smoreda, Z;
Musolesi, M;
(2018)
A comparison of spatial-based targeted disease mitigation strategies using mobile phone data.
EPJ Data Science
, 7
, Article 17. 10.1140/epjds/s13688-018-0145-9.
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Abstract
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 and mitigation 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 hypothetical 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 mitigation strategies to support decision-making during country-level epidemics.
| Type: | Article |
|---|---|
| Title: | A comparison of spatial-based targeted disease mitigation strategies using mobile phone data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1140/epjds/s13688-018-0145-9 |
| Publisher version: | http://dx.doi.org/10.1140/epjds/s13688-018-0145-9 |
| Language: | English |
| Additional information: | © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | Mathematical Methods In Social Sciences, Spatial networks, Mobile phone data, Human mobility, Epidemic spread, PATTERNS, PREDICTABILITY, TRANSMISSION, MIGRATION, NETWORKS, MALARIA, RWANDA, TRAVEL |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10036593 |
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