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

When spatial information is only reported at the level of large spatial zones like districts, regions, 592 or countries, no finer-scale information is available to inform which human cases transmitted 593 infection to one another between different localities. Here we explored how the size of these 594 spatial zones would affect inference for the monkeypox system by repeating the analysis using 595 spatial information at the district, region, or country resolution. The large differences in 596 parameter estimates generated under different broader contact zone assumptions in the 597 monkeypox analysis illustrates how sensitive inference results can be to the spatial scale 598 assumed for human-to-human transmission, and suggests that reporting spatial data at too large a 599 scale or ignoring inter-locality transmissions can lead to substantial estimate biases. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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 in surveillance effort might induce temporal autocorrelation among unlinked human cases, 608 giving rise to temporal clustering of cases that the model interprets as human-to-human 609 transmission. 610