Quantifying transmission of emerging zoonoses: Using mathematical models to maximize the value of surveillance data

Monique R. Ambrose; Adam J. Kucharski; Pierre Formenty; Jean-Jacques Muyembe-Tamfum; Anne W. Rimoin; James O. Lloyd-Smith

Introduction 54 Many recent infectious disease threats have been caused by pathogens with zoonotic 55 origins, including Ebola, pandemic H1N1 influenza, and SARS-and MERS-Coronaviruses, and 56 zoonotic pathogens are expected to be a primary source of future emerging infectious diseases 57 control measure, implemented at a known point in time, causes an abrupt reduction in spillover. 135 A related approach that requires knowledge of the human and animal reservoir population sizes 136 was also explored in Lo Iacono et al. [35] . Crucially, however, none of these methods 137 incorporate information about the spatial location of cases to improve inference power or to 138 estimate patterns of spatial spread. Spatial data is a powerful tool in transmission inference in 139 single-species studies (e.g. [36] [37] [38] [39] ), but has largely been excluded from analyses of zoonotic 140 transmission, which often implicitly assume homogenous mixing across the study area or that 141 human-to-human transmission can only occur within a locality. One recent exception to this is 142 the analysis by Cauchemez et al. [40] , which includes transmission at several spatial levels. 143