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

    90587.pdf (4.205Mb)
    Access Status
    Open access
    Authors
    Kraemer, M.U.G.
    Golding, Nick
    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.
    Reiner, R.C.
    Date
    2019
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Kraemer, M.U.G. and Golding, N. and Bisanzio, D. and Bhatt, S. and Pigott, D.M. and Ray, S.E. and Brady, O.J. et al. 2019. Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings. Scientific Reports. 9 (1): ARTN 5151.
    Source Title
    Scientific Reports
    DOI
    10.1038/s41598-019-41192-3
    ISSN
    2045-2322
    Faculty
    Faculty of Health Sciences
    School
    Curtin School of Population Health
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DE180100635
    URI
    http://hdl.handle.net/20.500.11937/90763
    Collection
    • Curtin Research Publications
    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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