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

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.

Automated summary

Human mobility is crucial for understanding its impact on various aspects, and spatial interaction models have been widely used for estimating travel when mobility data are limited. In this study, mobility patterns in Sub-Saharan African countries were analyzed using mobile phone data, and adjustments to the gravity model improved model fit, indicating that alternative models may be more effective in capturing observed mobility patterns.