Inferring incubation period distribution of COVID-19 based on SEAIR Model

TL;DR

This paper introduces a model-based approach using the SEAIR epidemic model to estimate COVID-19 incubation periods from reported case data, reducing biases inherent in survey methods.

Contribution

It presents a novel epidemic model-based method for inferring incubation periods, avoiding traditional survey biases and utilizing dynamic transmission data.

Findings

Estimated incubation period distribution for COVID-19.

Demonstrated the method's ability to reduce survey biases.

Provided insights into the variability of incubation periods.

Abstract

To reduce the biases of traditional survey-based methods, this paper proposes an epidemic model-based approach to inference the incubation period distribution of COVID-19 utilizing the publicly reported confirmed case number. We construct an epidemic model, namely SEAIR, and take advantage of the dynamic transmission process depicted by SEAIR to estimate the onset probability in each day of exposed individuals in eight impacted countries. Based on these estimations, the general incubation probability distribution of COVID-19 has been revealed. The proposed method can avoid several biases of traditional survey-based methods. However, due to the mathematical-model-based nature of this method, the inference results are somewhat sensitive to the setting of parameters. Therefore, this method should be practiced reasonably on the basis of a certain understanding of the studied epidemic.