Nonparametric serial interval estimation with uniform mixtures.
Uncover the hidden patterns in disease transmission with our innovative nonparametric approach. Unlock the secrets of the serial interval, a crucial metric for understanding infectious disease dynamics, without relying on restrictive parametric models.
This research presents a novel, fully data-driven methodology for estimating the serial interval distribution, a crucial parameter for understanding disease transmission dynamics. The proposed nonparametric estimator, based on uniform mixtures, overcomes the limitations of traditional parametric models by handling censored data effectively. The algorithms are simple, stable, and computationally efficient, making them easily implementable. The method complements existing approaches and provides a user-friendly tool for analyzing past, current, or future illness onset data, enabling researchers to gain deeper insights into the complexities of infectious disease transmission.