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Evaluating spatial interaction models for regional mobility in sub-Saharan Africa

Evaluating spatial interaction models for regional mobility in sub-Saharan Africa
Evaluating spatial interaction models for regional mobility in sub-Saharan Africa
Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations.
1553-734X
e1004267
Wesolowski, Amy
343b0df8-5a2f-46e2-9f1c-001d4adf7fb1
O’Meara, Wendy Prudhomme
48b9654e-3742-4794-83e0-21d6e17e347d
Eagle, Nathan
7936c351-0cae-47be-b0c1-e3f0f331d885
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Buckee, Caroline O.
f4bc891c-4f42-46a6-822d-03fc1f9cd55b
Ferrari, Matthew
a4743a12-f3e0-4ee3-9f03-f5a6e96be237
Ferrari, Matthew
a4743a12-f3e0-4ee3-9f03-f5a6e96be237
Wesolowski, Amy
343b0df8-5a2f-46e2-9f1c-001d4adf7fb1
O’Meara, Wendy Prudhomme
48b9654e-3742-4794-83e0-21d6e17e347d
Eagle, Nathan
7936c351-0cae-47be-b0c1-e3f0f331d885
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Buckee, Caroline O.
f4bc891c-4f42-46a6-822d-03fc1f9cd55b

Wesolowski, Amy, O’Meara, Wendy Prudhomme, Eagle, Nathan, Tatem, Andrew J. and Buckee, Caroline O. , Ferrari, Matthew (ed.) (2015) Evaluating spatial interaction models for regional mobility in sub-Saharan Africa. PLoS computational biology, 11 (7), e1004267. (doi:10.1371/journal.pcbi.1004267).

Record type: Article

Abstract

Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations.

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More information

e-pub ahead of print date: 9 July 2015
Organisations: Global Env Change & Earth Observation, WorldPop, Geography & Environment, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 378978
URI: https://eprints.soton.ac.uk/id/eprint/378978
ISSN: 1553-734X
PURE UUID: ba888966-ce90-4ad1-99e6-43230655aa7c
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 18 Jul 2015 15:15
Last modified: 29 Aug 2019 00:35

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Contributors

Editor: Matthew Ferrari
Author: Amy Wesolowski
Author: Wendy Prudhomme O’Meara
Author: Nathan Eagle
Author: Andrew J. Tatem ORCID iD
Author: Caroline O. Buckee

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