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Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study.

null nullJunerlyn Agua-AgumArchchun AriyarajahBruce AylwardLuke BawoPepe BilivoguiIsobel M BlakeRichard J BrennanAmy CawthorneEilish ClearyPeter ClementRoland ContehAnne CoriFoday DafaeBenjamin DahlJean-Marie DangouBoubacar DialloChristl A DonnellyIlaria DorigattiChristopher DyeTim EckmannsMosoka FallahNeil M FergusonLena FiebigChristophe FraserTini GarskeLice GonzalezEsther HamblionNuha HamidSara HerseyWes HinsleyAmara JambeiThibaut JombartDavid KargboSakoba KeitaMichael KinzerFred Kuti GeorgeBeatrice GodefroyGiovanna GutierrezNiluka KannangarageHarriet L MillsThomas MollerSascha MeijersYasmine MohamedOliver MorganGemma Nedjati-GilaniEmily NewtonPierre NouvelletTolbert NyenswahWilliam PereaDevin PerkinsSteven RileyGuenael RodierMarc RondyMaria SagradoCamelia SavulescuIlana J SchaferDirk SchumacherThomas SeylerAnita ShahMaria D Van KerkhoveC Samford WessehZabulon Yoti
Published in: PLoS medicine (2016)
Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population.
Keyphrases
  • endothelial cells
  • healthcare
  • primary care
  • electronic health record
  • induced pluripotent stem cells
  • emergency department
  • big data
  • machine learning
  • deep learning