Spatio-temporal dispersal patterns of SARS-CoV-2 in the Chinese mainland following the COVID-19 response adjustment
Year: 2026
Type: article
Abstract: OBJECTIVES: To characterize the early spatiotemporal transmission dynamics of SARS-CoV-2 following the Chinese government's adjustment of the long-standing "zero-COVID" policy in December 2022, focusing on both overseas importation and subsequent domestic dissemination, and to identify key factors driving these transmission pathways. METHODS: We analyzed 55,142 high-quality SARS-CoV-2 genomes from the Chinese mainland, covering six predominant Omicron sublineages (BA.5, BF.7, DY, XBB, EG.5, and HK) circulating during the policy-adjustment period. Genomes were grouped into three phases according to temporal shifts in lineage predominance. Mutation-network approaches were applied to infer spatiotemporal transmission patterns, and international air travel data were integrated to assess the contribution of global mobility to viral importation. RESULTS: South Korea, the United States, Japan, and several Asia-Pacific regions were the major sources of overseas introductions, with international passenger volume identified as the primary driver. Beijing and Shanghai functioned as key domestic transmission hubs at different stages. Beijing was predominantly affected during the early introduction phase, whereas Shanghai became the main hub later due to the highest inflow of international travelers. CONCLUSION: This study reveals the spatiotemporal dynamics and major drivers of SARS-CoV-2 transmission in the Chinese mainland in the post-"zero-COVID" period, underscoring the value of integrating genomic surveillance with mobility data to guide future preparedness and public health decision-making.
Authors: Chi Zhang, Jinfeng Zeng, Li Yin, Wenjie Han, Jingru Feng, Jialu Zheng, Zeqiang Zheng, Haoyu Long, Jing Tang, Yilin Chen, Xiangjun Du
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,900
OpenAlex ID: W7122619677
