Intensity and drivers of subtypes interference between seasonal influenza viruses in mainland China: A modeling study

Can Chen; Mengya Yang; Yu Wang; Daixi Jiang; Yuxia Du; Kexin Cao; Xiaobao Zhang; Xiaoyue Wu; Mengsha Chen; Yue You; Wenkai Zhou; Jiaxing Qi; Rui Yan; Changtai Zhu; Shigui Yang

iScience (Mar 2024)

Intensity and drivers of subtypes interference between seasonal influenza viruses in mainland China: A modeling study

Can Chen,
Mengya Yang,
Yu Wang,
Daixi Jiang,
Yuxia Du,
Kexin Cao,
Xiaobao Zhang,
Xiaoyue Wu,
Mengsha Chen,
Yue You,
Wenkai Zhou,
Jiaxing Qi,
Rui Yan,
Changtai Zhu,
Shigui Yang

Affiliations

Can Chen: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Mengya Yang: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Yu Wang: College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
Daixi Jiang: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Yuxia Du: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Kexin Cao: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Xiaobao Zhang: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Xiaoyue Wu: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Mengsha Chen: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Yue You: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Wenkai Zhou: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Jiaxing Qi: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Rui Yan: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
Changtai Zhu: Department of Transfusion Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China; Corresponding author
Shigui Yang: Department of Emergency Medicine, Second Affiliated Hospital, Department of Epidemiology and Biostatistics, School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China; Corresponding author

Journal volume & issue: Vol. 27, no. 3
p. 109323

Abstract

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Summary: Subtype interference has a significant impact on the epidemiological patterns of seasonal influenza viruses (SIVs). We used attributable risk percent [the absolute value of the ratio of the effective reproduction number (Rₑ) of different subtypes minus one] to quantify interference intensity between A/H1N1 and A/H3N2, as well as B/Victoria and B/Yamagata. The interference intensity between A/H1N1 and A/H3N2 was higher in southern China 0.26 (IQR: 0.11–0.46) than in northern China 0.17 (IQR: 0.07–0.24). Similarly, interference intensity between B/Victoria and B/Yamagata was also higher in southern China 0.14 (IQR: 0.07–0.24) than in norther China 0.10 (IQR: 0.04–0.18). High relative humidity significantly increased subtype interference, with the highest relative risk reaching 20.59 (95% CI: 6.12–69.33) in southern China. Southern China exhibited higher levels of subtype interference, particularly between A/H1N1 and A/H3N2. Higher relative humidity has a more pronounced promoting effect on subtype interference.

Published in iScience

ISSN: 2589-0042 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science
Website: http://www.cell.com/iscience/home

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