Quantitative assessment of Public Health and Social Measures implementation and relaxation on influenza transmission during COVID-19 in China: SEIABR and GBDT models.

Yuxi He, Kaiwei Luo, Han Ni, Wentao Kuang, Liuyi Fu, Shanghui Yi, Yuan Lv, Wenting Zha

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Article in Journal of global health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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8 authors.

Yuxi He *Hunan Key Laboratory of Molecular Epidemiology, School of Medicine, Hunan Normal University, Changsha, Hu Nan, China.

Kaiwei Luo *Hunan Provincial Center for Disease Control and Prevention (Hunan Academy of Preventive Medicine), Changsha, Hu Nan, China.

Han Ni *Hunan Key Laboratory of Molecular Epidemiology, School of Medicine, Hunan Normal University, Changsha, Hu Nan, China.

Wentao KuangHunan Key Laboratory of Molecular Epidemiology, School of Medicine, Hunan Normal University, Changsha, Hu Nan, China.

Liuyi FuHunan Key Laboratory of Molecular Epidemiology, School of Medicine, Hunan Normal University, Changsha, Hu Nan, China.

Shanghui YiHunan Key Laboratory of Molecular Epidemiology, School of Medicine, Hunan Normal University, Changsha, Hu Nan, China.

Yuan LvHunan Key Laboratory of Molecular Epidemiology, School of Medicine, Hunan Normal University, Changsha, Hu Nan, China.

Wenting ZhaHunan Key Laboratory of Molecular Epidemiology, School of Medicine, Hunan Normal University, Changsha, Hu Nan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Since 2019, China has implemented Public Health and Social Measures (PHSMs) to manage the coronavirus disease 2019 (COVID-19) outbreak. As the threat from SARS-CoV-2 diminished, these measures were relaxed, leading to increased respiratory infections and strained health care resources by mid-2023. Methods: The study utilised WHO's FluNet and Oxford's COVID-19 Government Response Tracker to assess how policy shifts have affected influenza. It examined changes in influenza incidence, subtype prevalence, and epidemic cycles over three periods: pre-COVID-19 and pre-PHSMs, during COVID-19 and PHSMs, and post-COVID-19 and post-PHSMs. The SEIABR model estimated the transmission probability () and real-time reproduction number () across these periods, while a gradient boosting decision tree (GBDT) analysed the effects of PHSM indicators on influenza transmission. Results: Results indicate that before PHSMs, the average incidence was 4.87 per 100 000, with a β-value of (7.95 ± 1.27) × 10 Conclusions: PHSMs effectively mitigated the spread of influenza, providing a foundation for future policy development to prevent respiratory diseases.

Indexed as

COVID-19Influenza, HumanPublic HealthChinaDecision TreesHumansIncidenceSARS-CoV-2

Identifiers

PMID39727104

DOI10.7189/jogh.14.05038

PMCPMC11672222

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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.