MERS-CoV spillover at the camel-human interface

Gytis Dudas; Luiz Max Carvalho; Andrew Rambaut; Trevor Bedford; Ali M. Somily; Mazin Barry; Sarah S. Al Subaie; Abdulaziz A. BinSaeed; Fahad A. Alzamil; Waleed Zaher; Theeb Al Qahtani; Khaldoon Al Jerian; Scott J.N. McNabb; Imad A. Al-Jahdali; Ahmed M. Alotaibi; Nahid A. Batarfi; Matthew Cotten; Simon J. Watson; Spela Binter; Paul Kellam

Although such epidemiological approaches yield important clues about exposure patterns and potential for larger outbreaks, much inevitably remains opaque to such approaches due to difficulties in linking cases into transmission clusters in the absence of detailed information. Where sequence data are relatively cheap to produce, genomic epidemiological approaches can fill this critical gap in outbreak scenarios (Liu et al., 2013; Gire et al., 2014; Grubaugh et al., 2017) . These data often yield a highly detailed picture of an epidemic when complete genome sequencing is performed consistently and appropriate metadata collected (Dudas et al., 2017) . Sequence data can help discriminate between multiple and single source scenarios (Gire et al., 2014; Quick et al., 2015) , which are fundamental to quantifying risk (Grubaugh et al., 2017) . Sequencing MERS-CoV has been performed as part of initial attempts to link human infections with the camel reservoir (Memish et al., 2014) , nosocomial outbreak investigations (Assiri et al., 2013b) and routine surveillance (Park et al., 2015) . A large portion of MERS-CoV sequences come from outbreaks within hospitals, where sequence data have been used to determine whether infections were isolated introductions or were part of a larger hospital-associated outbreak (Fagbo et al., 2015) . Similar studies on MERS-CoV have taken place at broader geographic scales, such as cities (Cotten et al., 2013) .