title: Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa.
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.
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America; Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Amy
authors.initials: A
authors.lastname: Wesolowski
authors.affiliation: Department of Medicine, Duke University and Duke Global Health Institute, Durham, North Carolina, United States of America.
authors.forename: Wendy Prudhomme
authors.initials: WP
authors.lastname: O'Meara
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America; College of Computer and Information Science, Northeastern University, Boston, Massachusetts, United States of America.
authors.forename: Nathan
authors.initials: N
authors.lastname: Eagle
authors.affiliation: Department of Geography and Environment, University of Southampton, Southampton, United Kingdom; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Tatem
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America; Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Caroline O
authors.initials: CO
authors.lastname: Buckee
mesh_terms: D017954:Africa South of the Sahara / Q000453:epidemiology; D040421:Cell Phone / Q000706:statistics & numerical data*; D003198:Computer Simulation; D004651:Employment / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D011157:Population Dynamics; D062211:Spatio-Temporal Analysis; D014195:Travel / Q000706:statistics & numerical data*