Epidemiological inference for emerging viruses using segregating sites

• Type of Material: Article
• Authors: Yeongseon Park, Emory University; Michael A Martin, Emory University; Katharina Koelle, Emory University
• Language: English
• Date: 2023-12-01
• Publisher: Springer Nature Limited
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41467-023-38809-7
• Title of Journal or Parent Work: Nature Communications
• Volume: 14
• Issue: 1
• Start Page: 3105
• End Page: 3105
• Keywords: SARS-CoV-2; Bayes Theorem; Basic Reproduction Number; Humans; COVID-19; Viruses; Communicable Diseases
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage.