Imperial College London
Alternate

Professor Neil Ferguson

Faculty of Medicine, School of Public Health

Director of the School of Public Health
 
 
 
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Contact

 

+44 (0)20 7594 3296neil.ferguson Website

 
 
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Location

 

508School of Public HealthWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ciavarella:2021:10.1371/journal.pcbi.1008588,
author = {Ciavarella, C and Ferguson, NM},
doi = {10.1371/journal.pcbi.1008588},
journal = {PLoS Computational Biology},
pages = {1--18},
title = {Deriving fine-scale models of human mobility from aggregated origin-destination flow data},
url = {http://dx.doi.org/10.1371/journal.pcbi.1008588},
volume = {17},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 distr
AU  - Ciavarella,C
AU  - Ferguson,NM
DO  - 10.1371/journal.pcbi.1008588
EP  - 18
PY  - 2021///
SN  - 1553-734X
SP  - 1
TI  - Deriving fine-scale models of human mobility from aggregated origin-destination flow data
T2  - PLoS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.1008588
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000618281000010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008588
UR  - http://hdl.handle.net/10044/1/87602
VL  - 17
ER  -
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