{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14"],"submitter":["He Y"],"pubmed_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 boos"],"journal":["Journal of global health"],"pagination":["05038"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11672222"],"repository":["biostudies-literature"],"pubmed_title":["Quantitative assessment of Public Health and Social Measures implementation and relaxation on influenza transmission during COVID-19 in China: SEIABR and GBDT models."],"pmcid":["PMC11672222"],"pubmed_authors":["Kuang W","He Y","Luo K","Ni H","Lv Y","Yi S","Fu L","Zha W"],"additional_accession":[]},"is_claimable":false,"name":"Quantitative assessment of Public Health and Social Measures implementation and relaxation on influenza transmission during COVID-19 in China: SEIABR and GBDT models.","description":"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 boos","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2025-04-21T13:48:17.91Z","creation":"2025-04-21T13:48:17.91Z"},"accession":"S-EPMC11672222","cross_references":{"pubmed":["39727104"],"doi":["10.7189/jogh.14.05038"]}}