Evidence map›Paper›PMID 39891052›Full record

ArticleBMC infectious diseases2025

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.

Jing Du, Lei Jia, Yanlin Gao, Jianting Su, Chao Wang, Xinghuo Pang, Gang Li

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jing DuInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, No. 16 Hepingli Middle Street, Beijing, 100013, China.
Lei JiaInstitute for Infectious and Endemic Disease Control, Beijing Center for Disease Prevention and Control, No. 16 Hepingli Middle Street, Beijing, 100013, China.
Yanlin GaoInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, No. 16 Hepingli Middle Street, Beijing, 100013, China.
Jianting SuInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, No. 16 Hepingli Middle Street, Beijing, 100013, China.
Chao WangInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, No. 16 Hepingli Middle Street, Beijing, 100013, China.
Xinghuo PangInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, No. 16 Hepingli Middle Street, Beijing, 100013, China.
Gang LiInstitute of Information and Statistics Center, Beijing Center for Disease Prevention and Control, No. 16 Hepingli Middle Street, Beijing, 100013, China. ligangcn@126.com.

Funding

Capital Health Research and Development of Special Fund 2024-2G-30121National Key Research and Development Program of China 2023YFC2308703
6 · The paper itself

Abstract

introductionUnderstanding the impact of public health and social measures (PHSMs) on influenza transmission is crucial for developing effective influenza prevention and control strategies.

methodsThis 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.

resultsThe 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.

conclusionsOur 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.

Indexed as

COVID-19Influenza, HumanPublic HealthBasic Reproduction NumberBeijingChinaHumansPandemicsSARS-CoV-2Attack rateInfluenzaPublic health and social measuresTransmission dynamics model

Identifiers

PMID39891052
PMCPMC11786347

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.