Characterizing human mobility patterns in rural settings of sub-Saharan Africa

eLife

Théophile Mande¹  C Jessica E Metcalf²  Caroline O Buckee³  Javier Perez-Saez⁴  John R Giles⁴  Hannah R Meredith⁴  Amy Wesolowski⁴  Simon Mutembo⁵  Andrea Rinaldo⁶  Andrew J Tatem⁷  Elliot N Kabalo⁸  Kabondo Makungo⁹ 

[1] Bureau d'Etudes Scientifiques et Techniques - Eau, Energie, Environnement (BEST-3E), Ouagadougou, Burkina Faso;Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, United States;Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States;Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States;Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States;Macha Research Trust, Choma, Zambia;Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Padova, Italy;Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom;Zambia Information and Communications Technology Authority, Lusaka, Zambia;Zamtel, Lusaka, Zambia;

关键词: Human mobility;    spatial models;    mobile phone data;    gravity model;    low and middle income countries;    Human;   

DOI  :  10.7554/eLife.68441

来源: eLife Sciences Publications, Ltd

【 摘 要 】

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

【 授权许可】

CC BY