Phylogeographic epidemiology of Dabie bandavirus in East Asia: divergent transmission networks and genotype‑linked clinical severity

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS), caused by Dabie bandavirus (SFTSV), exhibits geographically decoupled incidence and fatality patterns across East Asia. We aimed to elucidate the distinct ecological drivers and phylogeographic dynamics underlying this inland-coastal epidemiological divergence. Methods Integrating 1820 high-quality global genomes of SFTSV with well-characterized clinical cohorts (936 patients) and nationwide surveillance data (27,457 cases) from China, we constructed a comprehensive analytical framework. Ecological modeling, Bayesian phylogeography, and genotype–phenotype association analyses were employed to trace the evolutionary trajectories and clinical implications of the virus. Results A pronounced “inland-high-incidence vs. coastal-high-fatality” pattern of SFTS was identified. The incidence of SFTS exhibited divergent sensitivities to meteorological factors; inland transmission was sensitive to thermal fluctuations, whereas coastal dynamics were constrained by a sunshine threshold (> 200 h/month). In contrast, spatial divergence in clinical severity correlated with the distribution of regional viral genetic structures. Inland regions mainly co-circulated genotypes A, C, and D, while coastal regions were dominated by genotype B. Zhejiang province was identified as a genetic hub with significantly higher recombination frequencies than inland regions (11.0% vs. 3.5%, P < 0.001). Bayesian phylogeographic inference indicated frequent lineage exchange of Zhejiang province in China with the Republic of Korea and Japan. Clinically, genotypes B and D were associated with elevated mortality in coastal and inland regions, respectively, suggesting that the severe coastal phenotype is shaped by its genotype B-dominated structure. Additionally, the RdRp-N828S mutation emerged as a robust molecular correlate of fatal outcomes, warranting further functional validation. Conclusions Divergent meteorological factors and plausible maritime transmission networks may underlie the geographically decoupled epidemiology of SFTS. These findings highlight that risk assessment must extend beyond incidence alone and provide a phylogeographically informed framework for targeted surveillance and genotype-specific interventions in high-risk hotspots.

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Publication Details

Journal
Infectious Diseases of Poverty
Published
2026-09-11
DOI
https://doi.org/10.1186/s40249-026-01500-2
Primary Topic
Viral Infections and Vectors
Type
article
Field-Weighted Citation Impact
0.00

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article

Phylogeographic epidemiology of Dabie bandavirus in East Asia: divergent transmission networks and genotype‑linked clinical severity

Jiluo Liu, Hongxia Ni, Zhongfa Wang, Yi Shi et al.
Infectious Diseases of Poverty
Viral Infections and Vectors
article

Phylogeographic epidemiology of Dabie bandavirus in East Asia: divergent transmission networks and genotype‑linked clinical severity

Jiluo Liu, Hongxia Ni, Zhongfa Wang, Yi Shi, Guangwen Cao, Ping Li, Xiaojie Tan, Yibo Ding, Letian Fang, Jianhua Yin, Jimin Sun, Hanzheng Mu, Peng Li, Xin Yang, Wei Liu, Ling Ye
article en

Abstract

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS), caused by Dabie bandavirus (SFTSV), exhibits geographically decoupled incidence and fatality patterns across East Asia. We aimed to elucidate the distinct ecological drivers and phylogeographic dynamics underlying this inland-coastal epidemiological divergence. Methods Integrating 1820 high-quality global genomes of SFTSV with well-characterized clinical cohorts (936 patients) and nationwide surveillance data (27,457 cases) from China, we constructed a comprehensive analytical framework. Ecological modeling, Bayesian phylogeography, and genotype–phenotype association analyses were employed to trace the evolutionary trajectories and clinical implications of the virus. Results A pronounced “inland-high-incidence vs. coastal-high-fatality” pattern of SFTS was identified. The incidence of SFTS exhibited divergent sensitivities to meteorological factors; inland transmission was sensitive to thermal fluctuations, whereas coastal dynamics were constrained by a sunshine threshold (> 200 h/month). In contrast, spatial divergence in clinical severity correlated with the distribution of regional viral genetic structures. Inland regions mainly co-circulated genotypes A, C, and D, while coastal regions were dominated by genotype B. Zhejiang province was identified as a genetic hub with significantly higher recombination frequencies than inland regions (11.0% vs. 3.5%, P < 0.001). Bayesian phylogeographic inference indicated frequent lineage exchange of Zhejiang province in China with the Republic of Korea and Japan. Clinically, genotypes B and D were associated with elevated mortality in coastal and inland regions, respectively, suggesting that the severe coastal phenotype is shaped by its genotype B-dominated structure. Additionally, the RdRp-N828S mutation emerged as a robust molecular correlate of fatal outcomes, warranting further functional validation. Conclusions Divergent meteorological factors and plausible maritime transmission networks may underlie the geographically decoupled epidemiology of SFTS. These findings highlight that risk assessment must extend beyond incidence alone and provide a phylogeographically informed framework for targeted surveillance and genotype-specific interventions in high-risk hotspots.

Infectious Diseases of PovertyVol. 15(1)
Ministry of Education of the People's Republic of China (CN), Second Military Medical University (CN), Academy of Military Medical Sciences (CN), Hangzhou Center for Disease Control and Prevention (CN), Ningbo Center for Disease Control and Prevention (CN), Zhoushan Hospital (CN), Zhejiang Center for Disease Control and Prevention (CN), Taiwan Centers for Disease Control (TW)
National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality
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Openalex Percentile: Top 11%
Viral Infections and Vectors
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