Background: Influenza exhibits marked geographical heterogeneity in epidemic patterns, yet there is no globally standardized approach for determining epidemic thresholds. This study aimed to establish a region-specific epidemic intensity threshold system and evaluate its performance in influenza early warning and peak timing simulation.

Methods: Weekly influenza surveillance data from 167 countries (2009-2020) were obtained from the WHO Global Influenza Surveillance and Response System (GISRS). Three indicators were calculated for each of the 18 influenza transmission zones: ILI consultation rate (ILI rate), influenza virus positivity rate (IV positivity rate), and influenza incidence. MEM models were fitted according to each zone´s epidemic pattern to establish zone-specific threshold systems. Early warning and peak timing simulation performance were evaluated across target seasons. Model performance was assessed using leave-one-out cross-validation.

Results: Three epidemic patterns were identified: seasonal single-peak (North America, Europe, and Temperate South America), mixed single- and dual-peak (East Asia), and year-round or irregular (most African and tropical zones). Zone-specific four-level intensity thresholds were derived. The IV positivity rate demonstrated the most robust early warning performance, with alert-to-epidemic-start differences within ±2 weeks in most zones. Exact concordance or ±1 week difference accounted for the majority of observations. Peak timing simulation using influenza incidence achieved errors within ±3 weeks in 11 of 18 evaluable observations, concentrated in Europe and East Asia. Model sensitivity and specificity exceeded 80% in zones with distinct seasonality. The ILI rate performed adequately only in data-rich zones and showed limited utility in tropical and African settings.

Conclusion: The region-specific threshold system achieved early warning within ±2 weeks for the IV positivity rate in most zones, and peak timing errors within ±3 weeks in 11 of 18 evaluable observations, with sensitivity and specificity exceeding 80% in zones with distinct seasonality. The IV positivity rate is the most reliable indicator for global applications, while ILI-based indicators are best suited to zones with robust surveillance infrastructure and distinct seasonality. These findings support tailoring influenza early warning strategies to local epidemic characteristics.