- 1Heidelberg Institute of Global Health, Faculty of Medicine, Heidelberg University, Heidelberg, Germany (stella.dafka@uni-heidelberg.de)
- 2Interdisciplinary Center for Scientific Computing, Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- 3Department of Infectious Tropical Diseases and Microbiology, IRCCS Sacro Cuore Don Calabria Hospital, Negrar, Verona, Italy
- 4J.D. MacLean Centre for Tropical Diseases, McGill University, Montreal, Canada
- 5Department of Global Health, Boston University School of Public Health, Boston, Massachusetts
- 6Section of Infectious Disease, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
- 7Department of Infectious Diseases and Tropical Medicine, Division of Tropical Medicine and Clinical International Health, Hôpital Pellegrin, CHU Bordeaux, Bordeaux, France
- 8Global Health in the Global South - University of Bordeaux, National Institute for Health and Medical Research (INSERM) UMR 1219 - Research Institute for Sustainable Development (IRD) EMR 271, Bordeaux Population Health Research Centre, Bordeaux, France
Dengue has emerged as a significant public health challenge and the world's most prevalent climate-sensitive mosquito-borne disease. No antiviral drugs are currently available to treat the disease, but vaccine development has led to promising results in reducing dengue’s burden. As climate change is predicted to lead to geographic expansion of vector populations and increases in dengue outbreaks, the development of early warning systems is critical to improving outbreak preparedness to respond to dengue epidemics. Here, we investigate the remote response of tropical Indian Ocean sea surface temperature (SST) variability to dengue case counts in South Central Asia (SCA). More specifically, we provide new evidence on the association between the main modes of oceanic SST variability and dengue case counts using singular value decomposition (SVD) analysis. A cross-correlation analysis is then performed to quantify the maximum correlations and lags between SST climate indices and dengue case counts in SCA. We used traveler data from the GeoSentinel global infectious disease surveillance network and dengue case counts from the OpenDengue project. SST data was retrieved from the latest fifth generation global reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The results were compared with gridded SST datasets from observational reports and satellite data (HadISST1 and ERSSTv5). The SVD analysis reveals significant influence of SST anomalies on dengue case counts. The first leading SVD mode, which accounts for 25% of the total square covariance, represents the Indian Ocean basin mode, which is characterized by basin-wide warming and is statistically significantly correlated with dengue case counts. We found that positive SST anomalies over the western tropical Indian Ocean were associated with a surge in dengue cases in SCA after a lag time of 1-2 months. Our study demonstrated potential for predicting regional dengue epidemics based on remote SSTs. Combining dengue surveillance data and climatological data may be a promising mechanism to anticipate the geographic locations of future dengue outbreaks.
How to cite: Dafka, S., Huits, R., Libman, M., Hamer, D. H., Duvignaud, A., and Rocklöv, J.: Potential utility of Indian Ocean sea surface temperature for predicting dengue outbreaks in South Central Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6669, https://doi.org/10.5194/egusphere-egu25-6669, 2025.
