Common misspecification of the generation interval leads to reproduction number underestimation in phylodynamic inference

Generation intervals are distributions that describe the time between infection and onward transmission. They are a key epidemiological quantity because, together with the reproduction number R, they determine the population-level growth rate r of a pathogen and its doubling time. Conversely, when fitting epidemiological models to data, assumed generation intervals impact estimates of the reproduction number R. This is well-known from studies that have used case data for R inference, with many studies emphasizing the importance of choosing an accurate distribution for the generation interval. In phylodynamic inference of R, the generation interval distribution is often not explicitly mentioned, and the impact of generation interval misspecification has not been assessed. Here, we explore the impact of a commonly assumed (but generally misspecified) exponential generation interval distribution on the estimation of R in phylodynamic inference. Using simulations, we find that during viral exponential growth, estimates of R will be biased low if the generation interval is assumed to be exponentially distributed when it actually has a lower variance (as would be more generally expected). Furthermore, uncertainty in the biased R estimates will be smaller than expected. By fitting phylodynamic models with exponentially distributed generation intervals that are parameterized with inflated means, we further show that the underestimation of R can be explained by the quantitative relationship between R, r, and the generation interval distribution. Our work highlights the importance of acknowledging implicit generation interval assumptions in phylodynamic inference and points to the need for methodological developments in phylodynamic inference to provide greater flexibility in the specification of accurate generation intervals.