TY - JOUR TI - Characterizing human mobility patterns in rural settings of sub-Saharan Africa AU - Meredith, Hannah R AU - Giles, John R AU - Perez-Saez, Javier AU - Mande, Théophile AU - Rinaldo, Andrea AU - Mutembo, Simon AU - Kabalo, Elliot N AU - Makungo, Kabondo AU - Buckee, Caroline O AU - Tatem, Andrew J AU - Metcalf, C Jessica E AU - Wesolowski, Amy A2 - Flegg, Jennifer A2 - Walczak, Aleksandra M A2 - Flegg, Jennifer A2 - Rerolle, Francois VL - 10 PY - 2021 DA - 2021/09/17 SP - e68441 C1 - eLife 2021;10:e68441 DO - 10.7554/eLife.68441 UR - https://doi.org/10.7554/eLife.68441 AB - 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. KW - Human mobility KW - spatial models KW - mobile phone data KW - gravity model KW - low and middle income countries JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -