BackgroundTravel 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.MethodsWe 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.ResultsRoutinely 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 15 km, by 46% for 15–30 km and by 76% for distances greater than 30 km. 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.ConclusionsThe effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.

This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London and Hong Kong. By comparing whole cities typical patterns, we provide insights on how cultural, technological and economical factors shape human dynamics. At a more local scale, we use clustering analysis to identify locations with similar patterns within a city. Our research reveals a universal structure of cities, with core financial centers all sharing similar activity patterns and commercial or residential areas with more city-specific patterns. These findings hint that as the economy becomes more global, common patterns emerge in business areas of different cities across the globe, while the impact of local conditions still remains recognizable on the level of routine people activity.

Most mobile platforms of today enable the users to install third-party applications through application portals or stores. As the number of applications available increases, the users of mobile devices find it challenging to find new and relevant applications. The fact that these applications usually are browsed and downloaded from a mobile device, which has a smaller screen compared to desktop computers, makes this information overload even more intense. Recommender systems aid users in finding relevant applications. A challenge with such systems is that they traditionally need a user profile in order to produce recommendations, known as the new user problem. In this paper we present a context-aware recommender system for mobile applications which produces recommendations from the first use. This paper introduces context-based recommender concepts and presents a prototype implementation of said concepts.

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Functional clusters found in Los Angeles based on typical telecommunications activity, visualized on the interactive platform http://manycities.org.