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Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles.

Felipe J Colón-GonzálezLeonardo Soares BastosBarbara HofmannAlison HopkinQuillon HarphamTom CrockerRosanna AmatoIacopo FerrarioFrancesca MoschiniSamuel JamesSajni MaldeEleanor AinscoeVu Sinh NamDang Quang TanNguyen Duc KhoaMark HarrisonGina TsarouchiDarren LumbrosoOliver J BradyRachel Lowe
Published in: PLoS medicine (2021)
This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.
Keyphrases
  • zika virus
  • aedes aegypti
  • dengue virus
  • climate change
  • electronic health record
  • infectious diseases
  • big data