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

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

Prospective predictions with uncertainty.Forecasts from the national (1,0,0)(3,1,0)12 model with 95% Confidence Intervals are compared to reported dengue incidence (black, dashed lines). The (1,0,0)(2,1,0)12 model and models for each state are shown in Figure S2.
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f5: Prospective predictions with uncertainty.Forecasts from the national (1,0,0)(3,1,0)12 model with 95% Confidence Intervals are compared to reported dengue incidence (black, dashed lines). The (1,0,0)(2,1,0)12 model and models for each state are shown in Figure S2.

Mentions: Finally, we assessed the uncertainties in predictions (Fig. 5 - national, Supplementary Figure 2 – national and states). Forecasts for 1-month horizons had some ability to confidently distinguish a low season from a high season, and to a lesser extent at a 2-month horizon. However at 3-months and beyond, the expected number of cases in the peak transmission season generally ranged from zero or a very low number of cases to more cases than have ever been reported for both the local and common models.


Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico
Prospective predictions with uncertainty.Forecasts from the national (1,0,0)(3,1,0)12 model with 95% Confidence Intervals are compared to reported dengue incidence (black, dashed lines). The (1,0,0)(2,1,0)12 model and models for each state are shown in Figure S2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036038&req=5

f5: Prospective predictions with uncertainty.Forecasts from the national (1,0,0)(3,1,0)12 model with 95% Confidence Intervals are compared to reported dengue incidence (black, dashed lines). The (1,0,0)(2,1,0)12 model and models for each state are shown in Figure S2.
Mentions: Finally, we assessed the uncertainties in predictions (Fig. 5 - national, Supplementary Figure 2 – national and states). Forecasts for 1-month horizons had some ability to confidently distinguish a low season from a high season, and to a lesser extent at a 2-month horizon. However at 3-months and beyond, the expected number of cases in the peak transmission season generally ranged from zero or a very low number of cases to more cases than have ever been reported for both the local and common models.

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus