An international team has been improving dengue infection outbreak predictions, highlighting the challenges researchers face when attempting to build successful forecasting models.
Dengue is a mosquito-borne viral infection most commonly found in the Caribbean, the Americas, and South East Asia. The infection is usually mild, but can also cause severe flu-like symptoms and can be life-threatening. According to the World Health Organization, up to 100 million infections are estimated to occur annually in over 100 countries, putting almost half of the world’s population at risk.
Major dengue epidemics occur every two to five years depending on climate fluctuations. There is currently no definitive treatment, so prevention is tantamount to reducing the numbers of people affected. If scientists were able to predict an epidemic, healthcare providers would be better positioned to prevent and control it.
The US Pandemic Prediction and Forecasting Science and Technology Working Group established the Dengue Forecasting Project in 2015. Sixteen teams from around the world were given the same dengue incidence and climate data for Iquitos, Peru and San Juan, Puerto Rico in order to understand the universal drivers of Dengue epidemics. The researchers combined disease and climate data from previous years and the current season into complex models and tried to predict how the season would progress prior to the occurrence of outbreaks.
“Developing dengue forecasting models has so far been challenging,” explains Hokkaido University information scientist Matteo Convertino, who led one of the sixteen teams in the project. “Forecast targets often vary erratically, making it difficult to compare and evaluate them, and environmental variables are often tied weakly to population health targets.”