Journal article
Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings
MUG Kraemer, N Golding, D Bisanzio, S Bhatt, DM Pigott, SE Ray, OJ Brady, JS Brownstein, NR Faria, DAT Cummings, OG Pybus, DL Smith, AJ Tatem, SI Hay, RC Reiner
Scientific Reports | NATURE PORTFOLIO | Published : 2019
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..
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Awarded by Oxford Martin School, University of Oxford
Funding Acknowledgements
MUGK is funded by the Research for Health in Humanitarian Crises (R2HC) programme managed by ELRHA (to SIH). The R2HC programme is funded equally by the Wellcome Trust and the Department of International Development (DFID). MUGK is also supported by The Branco Weiss Fellowship -Society in Science, administered by the ETH Zurich and acknowledges funding from a Training Grant from the National Institute of Child Health and Human Development (T32HD040128) and the National Library of Medicine of the National Institutes of Health (R01LM010812, R01LM011965). NG, DB and DMP received funding from the Bill & Melinda Gates Foundation (#OPP10937011, #OPP1068048). NG was supported by a Discovery Early Career Researcher Award from the Australian Research Council (DE180100635). DLS and AJT are funded by the NIH/National Institute of Allergy and Infectious Diseases (#U10AI089674), and the Bill & Melinda Gates Foundation (AJT #OPP1106427, #1032350, DLS #OPP1110495). AJT is also supported by a Wellcome Trust Sustaining Health Grant (#10688/Z/15/Z). SIH is funded by a Senior Research Fellowship from the Wellcome Trust (#095066), and grants from the Bill & Melinda Gates Foundation (#OPP1119467, #OPP1093011, #OPP1106023 and #OPP1132415). DLS, AJT, SIH and RCR also acknowledge funding support from the RAPIDD programme of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center (FIC), National Institutes of Health (NIH). D.A.T.C. was supported by US NIH MIDAS program (U54-GM088491). OJB was funded by a Sir Henry Wellcome Fellowship funded by the Wellcome Trust (grant number 206471/Z/17/Z). This research received funding from the Oxford Martin School. Funding: Wellcome Trust.