Nonparametric serial interval estimation with uniform mixtures.

ABSTRACT: 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 methods mostly rely on parametric models. We present a fully data-driven methodology to estimate the serial interval distribution based on interval-censored serial interval data. The proposed nonparametric estimator of the cumulative distribution function of the serial interval is based on the class of uniform mixtures. Closed-form solutions are available for point estimates of different serial interval features and the bootstrap is used to construct confidence intervals. Algorithms underlying our approach are simple,

SUBMITTER: Gressani O 

PROVIDER: S-EPMC12338840 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature