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Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings

Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings
Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings

Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014–16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD’s incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.

2045-2322
Kraemer, M. U.G.
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Golding, N.
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Bisanzio, D.
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Bhatt, S.
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Pigott, D. M.
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Ray, S. E.
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Brady, O. J.
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Brownstein, J. S.
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Faria, N. R.
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Cummings, D. A.T.
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Pybus, O. G.
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Smith, D. L.
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Tatem, A. J.
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Hay, S. I.
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Reiner, R. C.
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Kraemer, M. U.G.
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Golding, N.
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Bisanzio, D.
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Bhatt, S.
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Pigott, D. M.
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Ray, S. E.
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Brady, O. J.
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Brownstein, J. S.
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Faria, N. R.
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Cummings, D. A.T.
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Pybus, O. G.
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Smith, D. L.
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Tatem, A. J.
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Hay, S. I.
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Reiner, R. C.
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Kraemer, M. U.G., Golding, N., Bisanzio, D., Bhatt, S., Pigott, D. M., Ray, S. E., Brady, O. J., Brownstein, J. S., Faria, N. R., Cummings, D. A.T., Pybus, O. G., Smith, D. L., Tatem, A. J., Hay, S. I. and Reiner, R. C. (2019) Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings. Scientific Reports, 9 (1). (doi:10.1038/s41598-019-41192-3).

Record type: Article

Abstract

Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014–16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD’s incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.

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s41598-019-41192-3 - Version of Record
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Accepted/In Press date: 3 March 2019
e-pub ahead of print date: 26 March 2019
Published date: 1 December 2019

Identifiers

Local EPrints ID: 430079
URI: https://eprints.soton.ac.uk/id/eprint/430079
ISSN: 2045-2322
PURE UUID: 01b57eeb-46da-4849-ade4-e8a15bd70aef
ORCID for A. J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 11 Apr 2019 16:30
Last modified: 13 Apr 2019 00:31

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Contributors

Author: M. U.G. Kraemer
Author: N. Golding
Author: D. Bisanzio
Author: S. Bhatt
Author: D. M. Pigott
Author: S. E. Ray
Author: O. J. Brady
Author: J. S. Brownstein
Author: N. R. Faria
Author: D. A.T. Cummings
Author: O. G. Pybus
Author: D. L. Smith
Author: A. J. Tatem ORCID iD
Author: S. I. Hay
Author: R. C. Reiner

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