Nonparametric serial interval estimation

The serial interval of an infectious disease is a key instrument to understand transmission dynamics. Estimation of the serial interval distribution from illness onset data extracted from transmission pairs is challenging due to the presence of censoring and state-of-the-art frequentist or Bayesian methods mostly rely on parametric models. We present a fully data-driven methodology to estimate the serial interval distribution based on (coarse) serial interval data. The proposal combines a nonparametric estimator of the cumulative distribution function with the bootstrap and yields point and interval estimates of any desired feature of the serial interval distribution. Algorithms underlying our approach are simple, fast and stable, and are thus easily implementable in any programming language most desired by modelers from the infectious disease community. The nonparametric routines are included in the EpiLPS package for ease of implementation. Our method complements existing parametric approaches for serial interval estimation and permits to straightforwardly analyze past, current, or future illness onset data streams.