A practical generation interval-based approach to inferring the strength of epidemics from their speed

Sang Woo Park; David Champredon; Joshua S. Weitz; Jonathan Dushoff

For example, during the Ebola outbreak in West Africa, many researchers tried to estimate R from r [2, 7, 30, 35, 18] but uncertainty in the generationinterval distribution was often neglected (but see [41] ). During the outbreak, [47] used a generation-interval argument to show that neglecting the effects of post-burial transmission would be expected to lead to underestimates of R. Our generation interval framework provides a clear interpretation of this result: as long as post-burial transmission tends to increase generation intervals, it should result in higher estimates of R for a given estimate of r. Knowing the exact shape of the generation interval distribution is difficult, but quantifying how various transmission routes and epidemic parameters 16 . CC-BY 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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/10.1101/312397 doi: bioRxiv preprint affect the moments of the generation interval distribution will help researchers better understand and predict the scope of future outbreaks. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/312397 doi: bioRxiv preprint 6 Appendix