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

In this work, we assumed that the spillover rate was homogenous through time and space, 622 but more complex disease dynamics in the reservoir or spatiotemporal heterogeneity in animal-623 human contacts may cause nontrivial deviations from this assumption in real-world systems. Of 624 particular concern is the possibility that outbreaks in the reservoir could cause periods of 625 amplified local spillover, which could create a clustering pattern of human cases potentially 626 indistinguishable from human-to-human transmission. Without information about disease 627 dynamics in the reservoir, accounting for this heterogeneous spillover will be challenging, but 628 . CC-BY 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/677021 doi: bioRxiv preprint cases contribute non-negligibly to transmission), the likelihood expression would need to be 651 modified substantially, and the lack of independence between cases might make a simulation-652 based inference approach necessary. 653