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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...

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Autores principales: Ciavarella, Constanze, Ferguson, Neil M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7920350/
https://www.ncbi.nlm.nih.gov/pubmed/33571187
http://dx.doi.org/10.1371/journal.pcbi.1008588
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author Ciavarella, Constanze
Ferguson, Neil M.
author_facet Ciavarella, Constanze
Ferguson, Neil M.
author_sort Ciavarella, Constanze
collection PubMed
description 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 ∼ 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.
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spelling pubmed-79203502021-03-09 Deriving fine-scale models of human mobility from aggregated origin-destination flow data Ciavarella, Constanze Ferguson, Neil M. PLoS Comput Biol Research Article 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 ∼ 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. Public Library of Science 2021-02-11 /pmc/articles/PMC7920350/ /pubmed/33571187 http://dx.doi.org/10.1371/journal.pcbi.1008588 Text en © 2021 Ciavarella, Ferguson http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ciavarella, Constanze
Ferguson, Neil M.
Deriving fine-scale models of human mobility from aggregated origin-destination flow data
title Deriving fine-scale models of human mobility from aggregated origin-destination flow data
title_full Deriving fine-scale models of human mobility from aggregated origin-destination flow data
title_fullStr Deriving fine-scale models of human mobility from aggregated origin-destination flow data
title_full_unstemmed Deriving fine-scale models of human mobility from aggregated origin-destination flow data
title_short Deriving fine-scale models of human mobility from aggregated origin-destination flow data
title_sort deriving fine-scale models of human mobility from aggregated origin-destination flow data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7920350/
https://www.ncbi.nlm.nih.gov/pubmed/33571187
http://dx.doi.org/10.1371/journal.pcbi.1008588
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