Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

CDC STACKS serves as an archival repository of CDC-published products including scientific findings, journal articles, guidelines, recommendations, or other public health information authored or co-authored by CDC or funded partners. As a repository, CDC STACKS retains documents in their original published format to ensure public access to scientific information.
i

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

Filetype[PDF-1.89 MB]


Details Supporting Files You May Also Like

Details:

  • Alternative Title:
    Sci Rep
  • Personal Author:
  • Description:
    Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.
  • Subjects:
  • Source:
    Sci Rep. 2016; 6.
  • Pubmed ID:
    27665707
  • Pubmed Central ID:
    PMC5036038
  • Document Type:
    Journal Article
  • Volume:
    6
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

You May Also Like

Checkout today's featured content at stacks.cdc.gov