@article {Park312397,
title = {Exploring how generation intervals link strength and speed of epidemics},
journal = {bioRxiv},
year = {2018},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Infectious-disease outbreaks are often characterized by the reproductive number R and exponential rate of growth r. The reproductive number R is of particular interest, because it provides information about how hard an outbreak will be to control, and about predicted final size. However, directly estimating R is difficult. In contrast, the rate of growth r can be estimated directly from incidence data while an outbreak is ongoing. R is typically estimated from r by using information about generation intervals {\textendash} that is, the amount of time between when an individual is infected by an infector, and when that infector was infected. In practice, it is infeasible to obtain the exact shape of a generation-interval distribution and it is not always qualitatively clear how changes in estimates of the distribution translate to changes in the estimate of R. Here, we show that parameterizing a generation interval distribution using its mean and variance provides a clear biological intuition into how its shape affects the relationship between R and r. We explore approximations based on estimates of the mean and variance of an underlying gamma distribution, and find that use of these two moments can be sufficient to capture the r-R relationship and provide robust estimates of R while an outbreak is ongoing.},
doi = {10.1101/312397},
url = {https://www.biorxiv.org/content/early/2018/05/02/312397},
author = {Park, Sang Woo and Champredon, David and Joshua Weitz and Jonathan Dushoff}
}