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 developed and tested a method to infer fundamental epidemiological 535 parameters and transmission patterns for zoonotic pathogens from epidemiological surveillance 536 data with aggregated spatial information. When tested against simulated datasets, the method 537 successfully recovered estimates of R and spillover rate close to the true values and also inferred 538 the fraction of cases resulting from zoonotic, within-locality, and between-locality sources with a 539 high degree of accuracy. The 'unknown denominator problem' that occurs when the total number 540 . CC-BY 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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 . https://doi.org/10.1101/677021 doi: bioRxiv preprint of localities under surveillance is unknown can cause large biases in parameter estimates, so we 541 modified the inference method to account for this observational process and enable unbiased 542 estimation in the presence of this common data gap. 543