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. 2014 Sep 29:6:ecurrents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e.
doi: 10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e.

Commentary: containing the ebola outbreak - the potential and challenge of mobile network data

Amy Wesolowski  1 , Caroline O Buckee  2 , Linus Bengtsson  3 , Erik Wetter  4 , Xin Lu  5 , Andrew J Tatem  6
Affiliations

Commentary: containing the ebola outbreak - the potential and challenge of mobile network data

Amy Wesolowski et al. PLoS Curr. .
No abstract available

Keywords: cellphone; disease model; disease outbreak; ebola; mobility; travel.

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Figures

Mobility patterns and connectivity in West Africa.
Mobility patterns and connectivity in West Africa.
A) Map showing the location of Ebola outbreaks in humans since 1976 (black dots) overlaid on a map of strength of connectivity measured by travel time to the nearest settlement of population 500,000 or more, with dense areas of low travel time indicative of high connectivity. No previously recorded Ebola outbreak has ever occurred in such a densely populated and large area of high connectivity as the ongoing outbreak that began in Guinea; B) Visualization of the flows of 500,000 mobile phone users between the (population-weighted) centres of sous-préfectures in Cote d’Ivoire. The inset highlights the mobility in the western border region (main figure: flows above 20 km with more than 10 average movements per day included, inset figure: flows above 20 km with at least one movement on average per day included); C) Outputs of a within-country mobility model for West Africa built on mobile phone CDRs. The lines show the flows predicted to be greater than 75-95% of the estimated flows per country between settlements for the average number of trips per week and are overlaid on a map of population density (www.worldpop.org.uk).
Figure S1. The time periods covered by the mobility datasets used to construct the version 1 Flowminder movement models. (Mig = Migration; MP = Mobile Phone).
Figure S1. The time periods covered by the mobility datasets used to construct the version 1 Flowminder movement models. (Mig = Migration; MP = Mobile Phone).
Figure S2. Predicted ranges of within-country mobility using the models parameterized on census microdata migration data (ipums), Cote d’Ivoire CDRs (CIV), Senegal CDRs (Sen) and Kenya CDRs (Kenya).
Figure S2. Predicted ranges of within-country mobility using the models parameterized on census microdata migration data (ipums), Cote d’Ivoire CDRs (CIV), Senegal CDRs (Sen) and Kenya CDRs (Kenya).
See this image and copyright information in PMC

References

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