{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:06:40Z","timestamp":1758672400621,"version":"3.44.0"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The COVID-19 pandemic has highlighted how political systems influence public health policy effectiveness. This study examines how different governance styles within China\u2019s Greater Bay Area shaped pandemic responses, comparing Guangdong Province, Hong Kong Special Administrative Region (SAR), and Macau SAR as representatives of society-mobilizing, market-facilitated, and targeted-control styles, respectively. This quasi-natural experiment setting controls for cultural and geographic variables while allowing for meaningful institutional comparison.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using a mixed-methods approach, the study analyzed COVID-19 infection statistics and 75,870 social media posts from Weibo and X (December 2019\u2013December 2022). The analysis employed statistical methods, sentiment analysis via the Baidu Application Programming Interface (API), and Latent Dirichlet Allocation (LDA) topic modeling to examine policy styles, effects, and public feedback. Interrupted time series (ITS) analysis was also applied to assess policy impacts across three distinct pandemic phases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Guangdong\u2019s society-mobilizing approach maintained stable case numbers (mean\u202f=\u202f31.03 daily cases in Guangzhou) with the highest sentiment scores (0.54) and 51.64% positive reactions. Hong Kong\u2019s market-facilitated approach showed the highest infection rates (mean\u202f=\u202f442.95) and lowest sentiment scores (0.46). Macau\u2019s targeted-control approach achieved the lowest infection rates (mean\u202f=\u202f0.79) with moderate sentiment scores (0.47). Interrupted time series (ITS) analysis revealed distinct transmission trends in each region, with significant changes observed during the Omicron phase in Guangzhou and Hong Kong, and sustained low transmission in Macau. Topic modeling identified region-specific concerns: overseas case imports (Guangdong), vaccine and local case monitoring (Hong Kong), and casino impacts (Macau).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The study demonstrates that effective pandemic response depends on governance\u2013society alignment, particularly during early outbreaks. While all three systems achieved relative success, their effectiveness varied based on institutional capacity, suggesting that successful crisis management requires consideration of political social structures while maintaining adaptability in transitioning from containment to endemic management. These findings offer a transferable framework for evaluating governance effectiveness in public health crises beyond the Greater Bay Area.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1654076","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T05:27:45Z","timestamp":1758605265000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fighting COVID-19 in China\u2019s Greater Bay Area: how do different policy styles affect policy effectiveness?"],"prefix":"10.3389","volume":"13","author":[{"given":"Zhuojun","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongquan","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-3-030-98985-9_10","article-title":"The policy and politics of public health in pandemics","volume":"1","author":"Fierlbeck","year":"2022","journal-title":"Integr Sci Polit Public Health"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.24321\/2455.7048.202025","article-title":"Politics of COVID-19: political epidemiology of and pandemics","volume":"5","author":"Kishore","year":"2020","journal-title":"Epidemiol Int"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1177\/2632077020938360","article-title":"Politics of prevention: reflections from the COVID-19 pandemic","volume":"1","author":"Romano","year":"2020","journal-title":"J Prev Health Promot"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1080\/17441690701727850","article-title":"Political epidemiology: strengthening socio-political analysis for mass immunisation\u2013lessons from the smallpox and polio programmes","volume":"4","author":"Taylor","year":"2009","journal-title":"Glob Public Health"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1080\/00472336.2023.2194322","article-title":"The politics of COVID-19: the political logic of China's zero-COVID policy","volume":"53","author":"Zhai","year":"2023","journal-title":"J Contemp Asia"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1017\/S0143814X24000163","article-title":"Authoritarian and democratic states: the COVID-19 pandemic and the efficacy of public health outcomes","volume":"44","author":"Ha","year":"2024","journal-title":"J Public Policy"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.jebo.2023.04.034","article-title":"Disease and democracy: political regimes and countries responsiveness to COVID-19","volume":"212","author":"Chen","year":"2023","journal-title":"J Econ Behav Organ"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"102957","DOI":"10.1016\/j.polgeo.2023.102957","article-title":"The politics of COVID-19: government response in comparative perspective","volume":"106","author":"Landman","year":"2023","journal-title":"Polit Geogr"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1186\/s12916-020-01790-9","article-title":"Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections","volume":"18","author":"Russell","year":"2020","journal-title":"BMC Med"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"e274270","DOI":"10.1371\/journal.pone.0274270","article-title":"The value conflict between freedom and security: explaining the variation of COVID-19 policies in democracies and autocracies","volume":"17","author":"Saam","year":"2022","journal-title":"PLoS One"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1016\/j.jebo.2021.03.018","article-title":"Political institutions and policy responses during a crisis","volume":"185","author":"Chiplunkar","year":"2021","journal-title":"J Econ Behav Organ"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1285552","DOI":"10.3389\/fpubh.2023.1285552","article-title":"Why do democracies respond differently to COVID-19? 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