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Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

Rachel Lowe1, Caio As Coelho2, Christovam Barcellos3

  • 1Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima, Barcelona, Spain.

Elife
|February 25, 2016
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Summary

A dengue early warning system accurately predicted high-risk periods in Brazil. The forecast model outperformed a null model, offering a valuable tool for public health services to manage dengue epidemics.

Keywords:
climatedengueearly warning systemepidemiologyevaluationglobal healthinfectious diseasemicrobiologymodelnoneprobabilistic

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Area of Science:

  • Epidemiology
  • Public Health
  • Infectious Disease Modeling

Background:

  • A prototype dengue early warning system was developed for Brazil prior to the 2014 World Cup.
  • The system aimed to provide probabilistic forecasts of dengue risk three months in advance.

Purpose of the Study:

  • To evaluate the accuracy of categorical dengue risk forecasts across Brazilian microregions.
  • To compare the performance of the forecast model against a null model based on historical dengue incidence.

Main Methods:

  • Validation of dengue forecasts using reported dengue cases from June 2014.
  • Comparison of forecast model performance (hits, misses) against a null model using seasonal averages.

Main Results:

  • The forecast model achieved a 57% hit rate in predicting high dengue risk.
  • The null model had a hit rate of 33% for predicting high dengue risk.
  • The forecast model demonstrated superior performance with more hits and fewer missed events compared to the null model.

Conclusions:

  • The dengue early warning model framework shows utility for public health interventions.
  • The model can aid in managing dengue risk before mass gatherings and peak seasons.
  • This framework can help control potentially explosive dengue epidemics through timely public health actions.