By Corey M Peak, Amy Wesolowski , Elisabeth zu Erbach-Schoenberg, Andrew J Tatem, Erik Wetter , Xin Lu, Daniel Power , Elaine Weidman-Grunewald , Sergio Ramos, Simon Moritz, Caroline O Buckee, and Linus Bengtsson.

Abstract

Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New ‘big data’ approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures.

We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national ‘stay at home’ lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis.

Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15km, by 46% for 15–30km and by 76% for distances greater than 30km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted.

The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymisation protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.

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