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

The model used in this study assumes that the number of new cases caused by an 1317 infectious individual follows a Poisson distribution, but previous work suggests that the offspring 1318 distribution is often better characterized by a negative binomial distribution, which allows for a 1319 greater amount of variation between individuals [1]. We simulated datasets using a negative 1320 binomial offspring distribution (using a dispersion parameter k=0.58 in accordance with previous 1321 . CC-BY 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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 estimates for monkeypox from [1]) and examined how well our inference method, which 1322 assumes a Poisson offspring distribution, estimated the true parameter values. Estimates for these 1323 datasets were only marginally less accurate than estimates for datasets generated with a Poisson 1324 offspring distribution (with an average percent error of 10.9% as opposed to 8.2% for R and of 1325 11.6% as opposed to 10.4% for spillover rate estimates) (S8 Fig, S3 Table) . As such, there are 1326 unlikely to be strong biases introduced from a mis-specified offspring distribution for the 1327 monkeypox dataset, though this bias could increase if applied to pathogens with more extreme 1328 transmission variance. 1329