Deriving fine-scale models of human mobility from aggregated origin-destination flow data

The spatial dynamics of epidemics are fundamentally affected by patterns of human mobility. Mobile phone call detail records (CDRs) are a rich source of mobility data, and allow semi-mechanistic models of movement to be parameterised even for resource poor settings. While the gravity model typically reproduces human movement reasonably well at the administrative level spatial scale, past studies suggest that parameter estimates vary with the level of spatial discretisation at which models are fitted. Given that privacy concerns usually preclude public release of very fine-scale movement data, such variation would be problematic for individual-based simulations of epidemic spread parametrised at a fine spatial scale. We therefore present new methods to fit fine-scale mathematical mobility models (here we implement variants of the gravity and radiation models) to spatially aggregated movement data and investigate how model parameter estimates vary with spatial resolution. We use gridded population data at 1km resolution to derive population counts at different spatial scales (down to similar to 5km grids) and implement mobility models at each scale. Parameters are estimated from administrative-level flow data between overnight locations in Kenya and Namibia derived from CDRs: where the model spatial resolution exceeds that of the mobility data, we compare the flow data between a particular origin and destination with the sum of all model flows between cells that lie within those particular origin and destination administrative units. Clear evidence of over-dispersion supports the use of negative binomial instead of Poisson likelihood for count data with high values. Radiation models use fewer parameters than the gravity model and better predict trips between overnight locations for both considered countries. Results show that estimates for some parameters change between countries and with spatial resolution and highlight how imperfect flow data and spatial population distribution can influence model fit. Author summary The growing use of large-scale individual-based models calls for reliable modelling of human population movement at ever finer scales. Mobility models have at times been fit to fine-scale movement data, such as travel questionnaires and GPS data. However, the restricted size of such datasets make them suboptimal for parametrising large-scale simulations. Larger datasets, such as census commuting data or mobile phone data, pose a different problem in that such datasets are usually made available at a much coarser spatial resolution than required for individual-based simulations. Here we present a straightforward, if computationally intensive, method to obtain fine-scale movement estimates from coarse-scale movement data. We trial the method on movement data from Kenya and Namibia and implement two of the most common mathematical mobility models, the gravity and the radiation models. Our findings confirm previous research that the parameter estimates for the mobility models differ across spatial scales and countries. We also investigate how population spatial distribution and the characteristics of the flow datasets influence parameter estimates.

Automated summary

The spread of epidemics is closely related to human mobility patterns, and models using data such as mobile phone call detail records can help to model human movement. Studies have found that parameter estimates can be influenced by spatial resolution levels, and model parameters may vary across different countries and spatial scales.