Variations in annual dengue intensities are explained by temperature anomalies.
| Title: | Variations in annual dengue intensities are explained by temperature anomalies. |
|---|---|
| Authors: | Porzucek AJ; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.; Public Health Modeling Unit, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Lopes R; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.; Public Health Modeling Unit, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Chew YT; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.; Public Health Modeling Unit, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Li K; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.; Public Health Modeling Unit, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Brady OJ; Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.; Warren JL; Public Health Modeling Unit, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Department of Biostatistics, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Carlson CJ; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.; Public Health Modeling Unit, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Weinberger DM; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.; Public Health Modeling Unit, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Grubaugh ND; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.; Public Health Modeling Unit, Yale School of Public Health, Yale University; New Haven, Connecticut, USA.; Department of Ecology and Evolutionary Biology, Yale University; New Haven, Connecticut, USA. |
| Source: | MedRxiv : the preprint server for health sciences [medRxiv] 2025 Dec 21. Date of Electronic Publication: 2025 Dec 21. |
| Publication Type: | Journal Article; Preprint |
| Language: | English |
| Journal Info: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| 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. |
| Grant Information: | DP2 AI176740 United States AI NIAID NIH HHS; F31 AI186435 United States AI NIAID NIH HHS |
| Entry Date(s): | Date Created: 20251225 Date Completed: 20260105 Latest Revision: 20260105 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC12723961 |
| DOI: | 10.64898/2025.12.19.25342670 |
| PMID: | 41445602 |
| Database: | MEDLINE |
Journal Article; Preprint