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Linus Bengtsson

Researcher at Karolinska Institutet

Publications -  48
Citations -  4103

Linus Bengtsson is an academic researcher from Karolinska Institutet. The author has contributed to research in topics: Population & Mobile phone. The author has an hindex of 22, co-authored 46 publications receiving 3144 citations. Previous affiliations of Linus Bengtsson include World Bank & University of Southampton.

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Journal ArticleDOI

Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus.

Moritz U. G. Kraemer, +49 more
- 04 Mar 2019 - 
TL;DR: It is shown that human movement patterns explain the spread of both Aedes aegypti and Aedes albopictus in Europe and the United States following their introduction and predicted the future distributions of both species in response to accelerating urbanization, connectivity and climate change.
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Predictability of population displacement after the 2010 Haiti earthquake

TL;DR: Analysis of the movements of 1.9 million mobile phone users in Haiti and the destinations of people who left the capital during the first three weeks after the earthquake suggest that population movements during disasters may be significantly more predictable than previously thought.
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Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti.

TL;DR: This work examines the use of mobile phone positioning data to monitor population movements during disasters and outbreaks, finding that reports on population movements can be generated within twelve hours of receiving data.
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Approaching the Limit of Predictability in Human Mobility

TL;DR: The findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
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Using mobile phone data to predict the spatial spread of cholera.

TL;DR: Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks, and may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.