Abstract
The global incidence of dengue has been rising during the past several decades as a result of increased risk as well as enhanced surveillance. This positively-sloped long-term trend in dengue cases has made it difficult to identify anomalously high intensity years. To address this issue, constructed a hierarchical Bayesian Poisson regression model to extract the long-term trend of annual incidence across 57 countries from 1990-2023 and quantify the difference between reported cases and baseline, which we call the Relative Intensity Score (RISc). To accommodate the peak transmission that often extends through December and January in the Southern Hemisphere, we used an annual time frame from July to June to determine RISc in this region (e.g., for these counties, the 2023-24 transmission season is listed as 2023). RISc provides a standardized measure of incidence intensity that adjusts for location- and time-specific contexts, thereby allowing intensities to be compared across geographies and timeframes. We found that globally, 1995, 1998, 2019, and 2023 represented the highest RISc years and that high RISc tends to follow multi-year cycles. Finally, we identified that temperature anomalies are most strongly associated with elevated RISc. This study provides the first standardized global analysis of dengue intensity, and provides a window into how spatial and temporal trends of dengue intensity may continue to evolve into the future.