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Deriving fine-scale models of human mobility from aggregated origin-destination flow data.
Constanze Ciavarella1, Neil M Ferguson1,2
1MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London.
Plos Computational Biology
|February 11, 2021
Summary
This study introduces methods to fit fine-scale mobility models using aggregated data, crucial for epidemic modeling in resource-poor settings. Radiation models outperform gravity models, offering better predictions despite variations in parameter estimates with spatial resolution.
Area of Science:
- Epidemiology
- Mobility modeling
- Spatial dynamics
Background:
- Human mobility patterns significantly influence epidemic spatial dynamics.
- Call detail records (CDRs) offer rich mobility data for semi-mechanistic models, even in resource-limited regions.
- Gravity models generally represent movement at administrative scales, but parameter estimates can vary with spatial resolution.
Purpose of the Study:
- To develop and evaluate methods for fitting fine-scale mathematical mobility models (gravity and radiation) to spatially aggregated movement data.
- To investigate how model parameter estimates change with varying spatial resolutions.
- To address challenges in parameterizing individual-based epidemic simulations due to privacy concerns and data aggregation.
Main Methods:
- Utilized gridded population data (1km resolution) to create population counts at various spatial scales (down to ~5km grids).
- Estimated parameters for gravity and radiation models using administrative-level flow data from CDRs in Kenya and Namibia.
- Adapted model fitting for cases where model spatial resolution exceeded mobility data resolution by summing flows between aggregated cells.
Main Results:
- Radiation models demonstrated superior predictive performance for overnight trips compared to gravity models in both Kenya and Namibia.
- Parameter estimates for mobility models showed variability across countries and spatial resolutions.
- Over-dispersion in count data supported the use of negative binomial over Poisson likelihoods for high-value counts.
Conclusions:
- Fine-scale mobility models can be effectively parameterized using aggregated data, enhancing epidemic modeling capabilities.
- The choice of model (radiation vs. gravity) and spatial resolution impacts parameter estimates and predictive accuracy.
- Imperfections in flow data and spatial population distribution influence model fitting and require careful consideration.
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