tetano
Editor, Senior Moderator
Arch Public Health
. 2026 Mar 3.
doi: 10.1186/s13690-026-01873-8. Online ahead of print.
Spatio-temporal analysis of influenza in old-new economic transition areas of coastal mega-cities: evidence from China
Ji Li[SUP] #[/SUP][SUP] 1 2 [/SUP], Zihan Mei[SUP] #[/SUP][SUP] 1 [/SUP], Lin Li[SUP] 3 [/SUP], Xiaochun Dong[SUP] 3 [/SUP], Yue Li[SUP] 1 2 [/SUP], Zhengkun Liu[SUP] 1 [/SUP], Jinyue Yuan[SUP] 1 [/SUP], Xiaohan Sun[SUP] 1 [/SUP], Xiangyu Yan[SUP] 4 [/SUP], Ying Zhang[SUP] 5 [/SUP], Chunxia Cao[SUP] 6 [/SUP]
Affiliations
Background: Long-term spatio-temporal analyses of influenza in coastal mega-cities remain limited. This study explores influenza dynamics in Tianjin, China (2010-2023) to inform prevention strategies.
Methods: The data was based on case data from Tianjin (2010-2023) and the Tianjin Statistical Yearbook. Temporal trends, spatial auto-correlation (global and local Moran's I), and spatio-temporal clusters (scan statistics) were assessed.
Results: From 2010 to 2023, Tianjin reported a cumulative total of 195,426 influenza cases. The eastern region displayed the top three prevalence indices among the sixteen districts. Spatial auto-correlation analyses indicated that the evolution of influenza incidence rates in Tianjin follows a pattern of being "high along the eastern coast". The analysis of spatio-temporal scanning identified the most significant cluster in eastern Tianjin, occurring from December 2017 to May 2019. This cluster covered four districts with a relative risk (RR) of 7.73 and log likelihood ratio (LLR) of 6597.49 (P < 0.001). Additionally, a similar cluster emerged in the eastern region of Tianjin from December 2019 to December 2023, covering three districts (RR: 27.41; LLR: 74011.43; P < 0.001).
Conclusions: Mega-city influenza prevention prioritizes high-economic-activity zones. Coastal mega-cities target influenza spread in old-new economic transition areas.
Keywords: Influenza; Mega-city; Spatial auto-correlation; Spatial epidemiology; Spatio-temporal scan analysis.
. 2026 Mar 3.
doi: 10.1186/s13690-026-01873-8. Online ahead of print.
Spatio-temporal analysis of influenza in old-new economic transition areas of coastal mega-cities: evidence from China
Ji Li[SUP] #[/SUP][SUP] 1 2 [/SUP], Zihan Mei[SUP] #[/SUP][SUP] 1 [/SUP], Lin Li[SUP] 3 [/SUP], Xiaochun Dong[SUP] 3 [/SUP], Yue Li[SUP] 1 2 [/SUP], Zhengkun Liu[SUP] 1 [/SUP], Jinyue Yuan[SUP] 1 [/SUP], Xiaohan Sun[SUP] 1 [/SUP], Xiangyu Yan[SUP] 4 [/SUP], Ying Zhang[SUP] 5 [/SUP], Chunxia Cao[SUP] 6 [/SUP]
Affiliations
- PMID: 41772678
- DOI: 10.1186/s13690-026-01873-8
Background: Long-term spatio-temporal analyses of influenza in coastal mega-cities remain limited. This study explores influenza dynamics in Tianjin, China (2010-2023) to inform prevention strategies.
Methods: The data was based on case data from Tianjin (2010-2023) and the Tianjin Statistical Yearbook. Temporal trends, spatial auto-correlation (global and local Moran's I), and spatio-temporal clusters (scan statistics) were assessed.
Results: From 2010 to 2023, Tianjin reported a cumulative total of 195,426 influenza cases. The eastern region displayed the top three prevalence indices among the sixteen districts. Spatial auto-correlation analyses indicated that the evolution of influenza incidence rates in Tianjin follows a pattern of being "high along the eastern coast". The analysis of spatio-temporal scanning identified the most significant cluster in eastern Tianjin, occurring from December 2017 to May 2019. This cluster covered four districts with a relative risk (RR) of 7.73 and log likelihood ratio (LLR) of 6597.49 (P < 0.001). Additionally, a similar cluster emerged in the eastern region of Tianjin from December 2019 to December 2023, covering three districts (RR: 27.41; LLR: 74011.43; P < 0.001).
Conclusions: Mega-city influenza prevention prioritizes high-economic-activity zones. Coastal mega-cities target influenza spread in old-new economic transition areas.
Keywords: Influenza; Mega-city; Spatial auto-correlation; Spatial epidemiology; Spatio-temporal scan analysis.