Genomic epidemiology of a densely sampled COVID-19 outbreak in China
Lily Geidelberg, Olivia Boyd, David Jorgensen, Igor Siveroni, Fabrícia F. Nascimento, Robert Johnson, Manon Ragonnet‐Cronin, Han Fu, Haowei Wang, Xiaoyue Xi, Wei Chen, Liu Dehui, Yingying Chen, Mengmeng Tian, Wei Tan, Junjie Zai, Wanying Sun, Jiandong Li, Junhua Li, Erik Volz, Xingguang Li, Qing Nie
Abstract
Abstract Analysis of genetic sequence data from the SARS-CoV-2 pandemic can provide insights into epidemic origins, worldwide dispersal, and epidemiological history. With few exceptions, genomic epidemiological analysis has focused on geographically distributed data sets with few isolates in any given location. Here, we report an analysis of 20 whole SARS- CoV-2 genomes from a single relatively small and geographically constrained outbreak in Weifang, People’s Republic of China. Using Bayesian model-based phylodynamic methods, we estimate a mean basic reproduction number (R0) of 3.4 (95% highest posterior density interval: 2.1–5.2) in Weifang, and a mean effective reproduction number (Rt) that falls below 1 on 4 February. We further estimate the number of infections through time and compare these estimates to confirmed diagnoses by the Weifang Centers for Disease Control. We find that these estimates are consistent with reported cases and there is unlikely to be a large undiagnosed burden of infection over the period we studied.