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Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data
Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data
Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.
0027-8424
201423542
Wesolowski, Amy
343b0df8-5a2f-46e2-9f1c-001d4adf7fb1
Metcalf, C. J. E.
96a07610-0061-4437-b73a-4b93c0f76955
Eagle, Nathan
7936c351-0cae-47be-b0c1-e3f0f331d885
Kombich, Janeth
965bbdc0-fc30-41df-9f3b-e7e7732cfbb8
Grenfell, Bryan T.
f80f3700-0b24-4932-80bf-d5ac2201882e
Bjørnstad, Ottar N.
a916ed51-d854-4aec-bda1-820d5e24a374
Lessler, Justin
bcf6536e-e3fb-4acb-820e-5c209f006b87
Tatem, Andrew J.
cac5d599-ac59-4a77-8dd9-b39f8428fdcb
Buckee, Caroline O.
f4bc891c-4f42-46a6-822d-03fc1f9cd55b
Wesolowski, Amy
343b0df8-5a2f-46e2-9f1c-001d4adf7fb1
Metcalf, C. J. E.
96a07610-0061-4437-b73a-4b93c0f76955
Eagle, Nathan
7936c351-0cae-47be-b0c1-e3f0f331d885
Kombich, Janeth
965bbdc0-fc30-41df-9f3b-e7e7732cfbb8
Grenfell, Bryan T.
f80f3700-0b24-4932-80bf-d5ac2201882e
Bjørnstad, Ottar N.
a916ed51-d854-4aec-bda1-820d5e24a374
Lessler, Justin
bcf6536e-e3fb-4acb-820e-5c209f006b87
Tatem, Andrew J.
cac5d599-ac59-4a77-8dd9-b39f8428fdcb
Buckee, Caroline O.
f4bc891c-4f42-46a6-822d-03fc1f9cd55b

Wesolowski, Amy, Metcalf, C. J. E., Eagle, Nathan, Kombich, Janeth, Grenfell, Bryan T., Bjørnstad, Ottar N., Lessler, Justin, Tatem, Andrew J. and Buckee, Caroline O. (2015) Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. Proceedings of the National Academy of Sciences, 201423542. (doi:10.1073/pnas.1423542112).

Record type: Article

Abstract

Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.

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More information

Accepted/In Press date: 21 July 2015
Published date: 2015
Organisations: Global Env Change & Earth Observation, Geography & Environment, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 380553
URI: https://eprints.soton.ac.uk/id/eprint/380553
ISSN: 0027-8424
PURE UUID: dd55d342-f552-436a-9fd8-2b883a609238

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Date deposited: 16 Sep 2015 12:55
Last modified: 17 Jul 2017 20:34

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Contributors

Author: Amy Wesolowski
Author: C. J. E. Metcalf
Author: Nathan Eagle
Author: Janeth Kombich
Author: Bryan T. Grenfell
Author: Ottar N. Bjørnstad
Author: Justin Lessler
Author: Andrew J. Tatem
Author: Caroline O. Buckee

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