Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study
Abstract Introduction Understanding the impact of public health and social measures (PHSMs) on influenza transmission is crucial for developing effective influenza prevention and control strategies. Methods This modeling study analyzed data from 2017 to 2022, in Beijing, China. Weekly influenza posi...
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BMC
2025-01-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | https://doi.org/10.1186/s12879-025-10505-5 |
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| author | Jing Du Lei Jia Yanlin Gao Jianting Su Chao Wang Xinghuo Pang Gang Li |
| author_facet | Jing Du Lei Jia Yanlin Gao Jianting Su Chao Wang Xinghuo Pang Gang Li |
| author_sort | Jing Du |
| collection | DOAJ |
| description | Abstract Introduction Understanding the impact of public health and social measures (PHSMs) on influenza transmission is crucial for developing effective influenza prevention and control strategies. Methods This modeling study analyzed data from 2017 to 2022, in Beijing, China. Weekly influenza positive rate and influenza-like rate were incorporated to quantify the community-level influenza activities. The effective reproduction number and influenza attack rate were estimated using a branching process model and a transmission dynamics model, respectively. The impact of PHSMs was quantified through log-linear regression and counterfactual simulations under varying PHSM scenarios. Results The transmissibility of influenza decreased by 68.41% (95%CI: 52.43, 78.80) in 2020, 67.07% (95%CI: 50.80, 77.89) in 2021 and 79.08% (95%CI: 63.18, 88.06) in 2022, and the attack rate dropped by 93.47% (95%CI: 85.86, 95.78), 95.37% (95%CI: 94.30, 96.89) and 71.61% (95%CI: 42.96, 81.24) over the same period, primarily due to the PHSMs. The simulation shows that strict PHSMs effectively suppressed the current flu epidemic effectively. When susceptible individuals drop to 50%, a relaxed strategy results in a smaller rebound in the next flu season, with epidemic sizes increasing to 1.18 (1.10, 1.30), 1.41 (1.20, 1.54), and 1.54 (1.35, 1.55) for relaxed, moderate, and strict measures, respectively. Conclusions Our study confirms the suppressive effect of coronavirus disease 2019 PHSMs on influenza transmission in Beijing. However, the relaxation of these measures’ triggers resurgence, emphasizing the need for adaptive control strategies tailored to the population susceptibility and epidemic dynamics. |
| format | Article |
| id | doaj-art-96a5652b382943598b8b8e9690763b24 |
| institution | Kabale University |
| issn | 1471-2334 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj-art-96a5652b382943598b8b8e9690763b242025-02-02T12:10:28ZengBMCBMC Infectious Diseases1471-23342025-01-0125111010.1186/s12879-025-10505-5Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling studyJing Du0Lei Jia1Yanlin Gao2Jianting Su3Chao Wang4Xinghuo Pang5Gang Li6Institute of Information and Statistics Center, Beijing Center for Disease Prevention and ControlInstitute for Infectious and Endemic Disease Control, Beijing Center for Disease Prevention and ControlInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and ControlInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and ControlInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and ControlInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and ControlInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and ControlAbstract Introduction Understanding the impact of public health and social measures (PHSMs) on influenza transmission is crucial for developing effective influenza prevention and control strategies. Methods This modeling study analyzed data from 2017 to 2022, in Beijing, China. Weekly influenza positive rate and influenza-like rate were incorporated to quantify the community-level influenza activities. The effective reproduction number and influenza attack rate were estimated using a branching process model and a transmission dynamics model, respectively. The impact of PHSMs was quantified through log-linear regression and counterfactual simulations under varying PHSM scenarios. Results The transmissibility of influenza decreased by 68.41% (95%CI: 52.43, 78.80) in 2020, 67.07% (95%CI: 50.80, 77.89) in 2021 and 79.08% (95%CI: 63.18, 88.06) in 2022, and the attack rate dropped by 93.47% (95%CI: 85.86, 95.78), 95.37% (95%CI: 94.30, 96.89) and 71.61% (95%CI: 42.96, 81.24) over the same period, primarily due to the PHSMs. The simulation shows that strict PHSMs effectively suppressed the current flu epidemic effectively. When susceptible individuals drop to 50%, a relaxed strategy results in a smaller rebound in the next flu season, with epidemic sizes increasing to 1.18 (1.10, 1.30), 1.41 (1.20, 1.54), and 1.54 (1.35, 1.55) for relaxed, moderate, and strict measures, respectively. Conclusions Our study confirms the suppressive effect of coronavirus disease 2019 PHSMs on influenza transmission in Beijing. However, the relaxation of these measures’ triggers resurgence, emphasizing the need for adaptive control strategies tailored to the population susceptibility and epidemic dynamics.https://doi.org/10.1186/s12879-025-10505-5Public health and social measuresInfluenzaTransmission dynamics modelAttack rate |
| spellingShingle | Jing Du Lei Jia Yanlin Gao Jianting Su Chao Wang Xinghuo Pang Gang Li Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study BMC Infectious Diseases Public health and social measures Influenza Transmission dynamics model Attack rate |
| title | Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study |
| title_full | Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study |
| title_fullStr | Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study |
| title_full_unstemmed | Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study |
| title_short | Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study |
| title_sort | assessment of the impacts of public health and social measures on influenza activity during the covid 19 pandemic from 2020 to 2022 in beijing china a modelling study |
| topic | Public health and social measures Influenza Transmission dynamics model Attack rate |
| url | https://doi.org/10.1186/s12879-025-10505-5 |
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