# Weibull-Type Incubation Period and Time of Exposure Using γ-Divergence

## TL;DR

This paper introduces a robust statistical method to estimate exposure time and incubation period during disease outbreaks, even when data includes secondary infections.

## Contribution

A novel robust estimation framework using a three-parameter Weibull distribution and γ-divergence for exposure time and incubation period estimation.

## Key findings

- The proposed method outperforms conventional approaches in bias and mean squared error in simulations.
- The method shows practical advantages when applied to real-world COVID-19 surveillance data.
- It is robust to data contamination from secondary or tertiary infections.

## Abstract

Accurately determining the exposure time to an infectious pathogen, together with the corresponding incubation period, is vital for identifying infection sources and implementing targeted public health interventions. However, real-world outbreak data often include outliers—namely, tertiary or subsequent infection cases not directly linked to the initial source—that complicate the estimation of exposure time. To address this challenge, we introduce a robust estimation framework based on a three-parameter Weibull distribution in which the location parameter naturally corresponds to the unknown exposure time. Our method employs a γ-divergence criterion—a robust generalization of the standard cross-entropy criterion—optimized via a tailored majorization–minimization (MM) algorithm designed to guarantee a monotonic decrease in the objective function despite the non-convexity typically present in robust formulations. Extensive Monte Carlo simulations demonstrate that our approach outperforms conventional estimation methods in terms of bias and mean squared error as well as in estimating the incubation period. Moreover, applications to real-world surveillance data on COVID-19 illustrate the practical advantages of the proposed method. These findings highlight the method’s robustness and efficiency in scenarios where data contamination from secondary or tertiary infections is common, showing its potential value for early outbreak detection and rapid epidemiological response.