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Scientific Reports


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.





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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.


This work was supported by the National Institute of General Medical Sciences at the National Institutes of Health (Grant 1U54GM088558), the National Institute of Allergy and Infectious Diseases at the National Institutes of Health (Grants R21 AI115173-01 and R01 AI102939-01), and the National Library of Medicine at the National Institutes of Health (Grant R01 LM010812-04).