Publication date: Jan 01, 2026
COVID-19 pandemics have greatly impacted the epidemiology of seasonal influenza, presenting a challenge for postpandemic prediction and prevention and control of influenza. This study aimed to gain a deeper understanding of future influenza patterns. 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. 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
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | influenza |
| disease | MESH | COVID-19 pandemic |
| pathway | REACTOME | Reproduction |
| disease | MESH | Long Covid |