December 2023 Estimating Covid-19 transmission time using Hawkes point processes
Frederic Schoenberg
Author Affiliations +
Ann. Appl. Stat. 17(4): 3349-3362 (December 2023). DOI: 10.1214/23-AOAS1765

Abstract

The question addressed here is whether, using Hawkes models. the distribution of SARS-CoV-2 (Covid-19) transmission times can be estimated accurately with only case-count data. We fit Hawkes models with varying productivities to each of the 50 United States individually, estimating for each state a transmission time density, both nonparametrically and using a normal approximation. We find that, for nearly all states, the estimated transmission times are centered near seven days with a standard deviation of approximately one day. Compared to previous reports, the results here suggest that transmission times for SARS-CoV-2 are somewhat shorter, on average, and the distribution is less diffuse, though the results also suggest the possibility of transmission occurring on the first day of exposure.

Funding Statement

The author was supported by NSF Grant DMS-2124433.

Acknowledgments

The author thanks the CDC for providing the data for our analysis. Computations were performed in R. The code for our simulations and computations is in Section 1 of the Supplementary Material (Schoenberg (2023)).

Citation

Download Citation

Frederic Schoenberg. "Estimating Covid-19 transmission time using Hawkes point processes." Ann. Appl. Stat. 17 (4) 3349 - 3362, December 2023. https://doi.org/10.1214/23-AOAS1765

Information

Received: 1 September 2021; Revised: 1 March 2023; Published: December 2023
First available in Project Euclid: 30 October 2023

MathSciNet: MR4661701
Digital Object Identifier: 10.1214/23-AOAS1765

Keywords: Disease epidemics , Hawkes model , point process , Self-exciting

Rights: Copyright © 2023 Institute of Mathematical Statistics

JOURNAL ARTICLE
14 PAGES

This article is only available to subscribers.
It is not available for individual sale.
+ SAVE TO MY LIBRARY

Vol.17 • No. 4 • December 2023
Back to Top