A groundbreaking study led by Emilie Finch from the London School of Hygiene and Tropical Medicine and Rachel Lowe from the Barcelona Supercomputing Centre has developed a forecasting model capable of predicting dengue outbreak risks in Singapore. This model, created in collaboration with Singapore’s National Environment Agency, integrates climate data and information on dengue virus serotypes to enhance prediction accuracy.
Dengue, a mosquito-borne disease, poses a significant public health challenge, particularly in Southeast Asia. The spread of dengue has been exacerbated by rising global temperatures and altered rainfall patterns, with 2024 marking a record 14 million cases worldwide. The new model leverages over 20 years of data, revealing that dengue outbreak risks peak during El Niño conditions and following shifts in dominant dengue serotypes.
The study also evaluated the impact of Project Wolbachia, which releases Wolbachia-carrying mosquitoes to curb dengue transmission. The model estimated that approximately 28% of expected dengue cases in 2023 were prevented due to expanded releases in 2022. Rachel Lowe highlighted the model’s ability to provide early warnings, stating, “This integrated approach allows us to anticipate surges in dengue weeks in advance and provide actionable early warnings.”
Looking forward, the research team aims to compare this model with other forecasting tools and explore its application in different regions. This interdisciplinary effort underscores the potential of combining computational modelling, climate science, and public health to address the challenges posed by climate change and disease outbreaks.
