한빛사논문
- 조회462
Yeongseon Park 1, Michael A. Martin 1,4 & Katia Koelle 2,3
1Graduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, GA 30322, USA.
2Department of Biology, Emory University, Atlanta, GA 30322, USA.
3Emory Center of Excellence for Influenza Research and Response (CEIRR), Atlanta, GA, USA.
4Present address: Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Corresponding author : Correspondence to Katia Koelle.
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.
논문정보
- Research article
- 2023년 05월 (BRIC 등록일 2023-06-14)
- 국외 연구진
- 생명과학 > 분류/생태/환경생물학
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