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Interactions of SARS-CoV-2, influenza and respiratory syncytial virus influence epidemic timing and risk

Interactions of SARS-CoV-2, influenza and respiratory syncytial virus influence epidemic timing and risk
Interactions of SARS-CoV-2, influenza and respiratory syncytial virus influence epidemic timing and risk
Background: interactions between SARS-CoV-2, influenza virus, and respiratory syncytial virus (RSV) at the population level remain poorly understood. This study aimed to quantify potential interactions among these viruses and assess their influence on transmission dynamics.

Methods: we analyzed weekly surveillance data on SARS-CoV-2, influenza A and B viruses (IAV and IBV), and RSV from seven regions from October 2021 to May 2024. Distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical framework were used to assess the exposure-lag-response associations among virus pairs. Additionally, we developed a two-pathogen, meta-population mechanistic transmission model to capture the co-epidemic dynamics of IAV and SARS-CoV-2, and to quantify the strength and duration of their bidirectional interactions.

Results: among all virus pairs examined, a statistically significant association is identified only between IAV positivity and subsequent SARS-CoV-2 risk. When IAV positive rate percentile is between the 52nd and 88th percentiles, the relative risk (RR) of SARS-CoV-2 infection is significantly reduced. The lowest RR for SARS-CoV-2 (0.58, 95% CrI: 0.40-0.85) occurs at a 5-week lag when IAV positivity reaches the 70th percentile. The fitted mechanistic model using incidence data in Beijing shows that IAV infection substantially reduces infection to SARS-CoV-2 by 94.24% (95% CrI: 88.50%–99.24%), with the protective effect lasting 38.24 days (95% CrI: 35.50–41.29 days). Conversely, SARS-CoV-2 infection is associated with a slight increase in infection to IAV.

Conclusions: our findings indicate that IAV circulation may transiently reduce population-level infection to SARS-CoV-2, potential through ecological or immunological mechanisms.
2730-664X
Liu, Yonghong
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Wang, Xiaoli
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Li, Mengyao
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Cleary, Eimear
3cbf7016-269e-4517-ab4f-323e86db6e58
Cheng, Zhifeng
d9d2cbb1-163a-46c9-b587-144e20b415d2
Zhang, Wenbin
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Shen, Ying
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Yao, Hui
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Han, Jiatong
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Ruktanonchai, Nick W.
Tatem, Andrew J.
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Lai, Shengjie
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Wang, Quanyi
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Yang, Peng
3fc33b62-2759-4806-9e1d-58e1365d6857
Liu, Yonghong
2dd3a744-7498-4ead-8e5c-fa7395e9b195
Wang, Xiaoli
a4d690b5-875d-4e09-b4f1-0b6953839bf3
Li, Mengyao
d11fb612-4850-4265-82b1-4be6ef28b8a5
Cleary, Eimear
3cbf7016-269e-4517-ab4f-323e86db6e58
Cheng, Zhifeng
d9d2cbb1-163a-46c9-b587-144e20b415d2
Zhang, Wenbin
a4ab325c-e9cb-4369-959b-25a3320bb4e3
Shen, Ying
3dcc0427-d2fa-4161-a446-fb5d9108ba43
Yao, Hui
a91048bd-ab24-4a18-94c5-a6cdf6a2e66e
Han, Jiatong
d0474d87-6211-474c-9183-97ad74d0e065
Ruktanonchai, Nick W.
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Wang, Quanyi
d79e773f-db3a-4256-a113-94cb94466ed4
Yang, Peng
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Liu, Yonghong, Wang, Xiaoli, Li, Mengyao, Cleary, Eimear, Cheng, Zhifeng, Zhang, Wenbin, Shen, Ying, Yao, Hui, Han, Jiatong, Ruktanonchai, Nick W., Tatem, Andrew J., Lai, Shengjie, Wang, Quanyi and Yang, Peng (2026) Interactions of SARS-CoV-2, influenza and respiratory syncytial virus influence epidemic timing and risk. Nature Communications Medicine. (doi:10.1038/s43856-026-01504-x).

Record type: Article

Abstract

Background: interactions between SARS-CoV-2, influenza virus, and respiratory syncytial virus (RSV) at the population level remain poorly understood. This study aimed to quantify potential interactions among these viruses and assess their influence on transmission dynamics.

Methods: we analyzed weekly surveillance data on SARS-CoV-2, influenza A and B viruses (IAV and IBV), and RSV from seven regions from October 2021 to May 2024. Distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical framework were used to assess the exposure-lag-response associations among virus pairs. Additionally, we developed a two-pathogen, meta-population mechanistic transmission model to capture the co-epidemic dynamics of IAV and SARS-CoV-2, and to quantify the strength and duration of their bidirectional interactions.

Results: among all virus pairs examined, a statistically significant association is identified only between IAV positivity and subsequent SARS-CoV-2 risk. When IAV positive rate percentile is between the 52nd and 88th percentiles, the relative risk (RR) of SARS-CoV-2 infection is significantly reduced. The lowest RR for SARS-CoV-2 (0.58, 95% CrI: 0.40-0.85) occurs at a 5-week lag when IAV positivity reaches the 70th percentile. The fitted mechanistic model using incidence data in Beijing shows that IAV infection substantially reduces infection to SARS-CoV-2 by 94.24% (95% CrI: 88.50%–99.24%), with the protective effect lasting 38.24 days (95% CrI: 35.50–41.29 days). Conversely, SARS-CoV-2 infection is associated with a slight increase in infection to IAV.

Conclusions: our findings indicate that IAV circulation may transiently reduce population-level infection to SARS-CoV-2, potential through ecological or immunological mechanisms.

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s43856-026-01504-x_reference - Version of Record
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Submitted date: 1 February 2025
Accepted/In Press date: 24 February 2026
e-pub ahead of print date: 14 March 2026

Identifiers

Local EPrints ID: 510918
URI: http://eprints.soton.ac.uk/id/eprint/510918
ISSN: 2730-664X
PURE UUID: d9357cc3-1812-41a6-82ad-2e1cd4b73846
ORCID for Eimear Cleary: ORCID iD orcid.org/0000-0003-2549-8565
ORCID for Wenbin Zhang: ORCID iD orcid.org/0000-0002-9295-1019
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X
ORCID for Shengjie Lai: ORCID iD orcid.org/0000-0001-9781-8148

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Date deposited: 27 Apr 2026 16:34
Last modified: 28 Apr 2026 02:18

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Contributors

Author: Yonghong Liu
Author: Xiaoli Wang
Author: Mengyao Li
Author: Eimear Cleary ORCID iD
Author: Zhifeng Cheng
Author: Wenbin Zhang ORCID iD
Author: Ying Shen
Author: Hui Yao
Author: Jiatong Han
Author: Nick W. Ruktanonchai
Author: Andrew J. Tatem ORCID iD
Author: Shengjie Lai ORCID iD
Author: Quanyi Wang
Author: Peng Yang

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