tetano
Editor, Senior Moderator
Int J Infect Dis
. 2025 Oct 29:108173.
doi: 10.1016/j.ijid.2025.108173. Online ahead of print. Changes of Dynamics of Influenza before and after COVID-19 Pandemic: A Global Study in Eight Regions
Suyi Zhang[SUP] 1 [/SUP], Minyi Yang[SUP] 2 [/SUP], Wenyong Zhou[SUP] 1 [/SUP], Ye Yao[SUP] 3 [/SUP], Yifeng Shen[SUP] 4 [/SUP], Weibing Wang[SUP] 5 [/SUP]
Affiliations
Background: COVID-19 pandemics have greatly impacted the epidemiology of seasonal influenza, presenting a challenge for post-pandemic prediction and prevention and control of influenza. This study aimed to gain a deeper understanding of future influenza patterns.
Methods: We collected data from eight regions around the world. Using SVIRS model, we identified a clear change during and after the COVID-19 era. Linear regression modelling showed the relationship between influenza, COVID-19 and PHSMs. Finally, we conducted a simulation to assess the impact of implementing simple PHSMs at the end of the COVID-19 outbreak.
Results: In all regions except Tokyo, there were off-season influenza outbreaks. The levels of influenza outbreaks were found to be higher than simulated. In California, the peak was 8.5 times higher than the simulated value (95% CI: 1.00, 8.65). In every region, the peaks of influenza outbreaks closely followed those of the COVID-19 pandemic. The results of linear regression indicated that the influenza outbreaks were significantly correlated with that of COVID-19 and existing seasonality (P-value < 0.05). Finally, we conducted a simulation to assess the impact of implementing PHSMs at the end of the COVID-19 outbreak. Reducing the reproduction number of influenza to 95% of its original value could result in a 50% decrease in the peak.
Conclusion: This study highlights the impact of the COVID-19 pandemic on influenza, raises the possibility of incorporating the observed trends in the incidence of COVID-19 into influenza prediction models, and suggests the implementation of collaborative surveillance to mitigate the risk of influenza outbreaks.
Keywords: COVID-19; Influenza; collaborative surveillance; seasonality.
. 2025 Oct 29:108173.
doi: 10.1016/j.ijid.2025.108173. Online ahead of print. Changes of Dynamics of Influenza before and after COVID-19 Pandemic: A Global Study in Eight Regions
Suyi Zhang[SUP] 1 [/SUP], Minyi Yang[SUP] 2 [/SUP], Wenyong Zhou[SUP] 1 [/SUP], Ye Yao[SUP] 3 [/SUP], Yifeng Shen[SUP] 4 [/SUP], Weibing Wang[SUP] 5 [/SUP]
Affiliations
- PMID: 41173204
- DOI: 10.1016/j.ijid.2025.108173
Background: COVID-19 pandemics have greatly impacted the epidemiology of seasonal influenza, presenting a challenge for post-pandemic prediction and prevention and control of influenza. This study aimed to gain a deeper understanding of future influenza patterns.
Methods: We collected data from eight regions around the world. Using SVIRS model, we identified a clear change during and after the COVID-19 era. Linear regression modelling showed the relationship between influenza, COVID-19 and PHSMs. Finally, we conducted a simulation to assess the impact of implementing simple PHSMs at the end of the COVID-19 outbreak.
Results: In all regions except Tokyo, there were off-season influenza outbreaks. The levels of influenza outbreaks were found to be higher than simulated. In California, the peak was 8.5 times higher than the simulated value (95% CI: 1.00, 8.65). In every region, the peaks of influenza outbreaks closely followed those of the COVID-19 pandemic. The results of linear regression indicated that the influenza outbreaks were significantly correlated with that of COVID-19 and existing seasonality (P-value < 0.05). Finally, we conducted a simulation to assess the impact of implementing PHSMs at the end of the COVID-19 outbreak. Reducing the reproduction number of influenza to 95% of its original value could result in a 50% decrease in the peak.
Conclusion: This study highlights the impact of the COVID-19 pandemic on influenza, raises the possibility of incorporating the observed trends in the incidence of COVID-19 into influenza prediction models, and suggests the implementation of collaborative surveillance to mitigate the risk of influenza outbreaks.
Keywords: COVID-19; Influenza; collaborative surveillance; seasonality.