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Dengue, a mosquito-borne virus that affects an estimated 390 million people globally every year, has seen a sharp increase in case numbers over the past two decades. While the disease is prevalent worldwide, the most severe outbreaks have been concentrated in tropical regions, particularly South America. To better understand the factors contributing to the spread of dengue and to improve prevention measures, researchers are turning to climate data in combination with machine learning techniques.
A recent study published in The European Physical Journal Special Topics, led by Enrique Gabrick at the Potsdam Institute for Climate Impact, Germany, shows how integrating climate variables can refine the accuracy of dengue forecasts. However, the success of this approach varies by region, highlighting the complexity of predicting disease outbreaks across diverse geographical areas.
The number of dengue infections has surged significantly in recent years, increasing from 500,000 cases in 2000 to a staggering 5.2 million in 2019. “These increases are largely driven by environmental conditions—specifically, temperature, humidity, and precipitation—vital factors in the mosquito life cycle,” Gabrick explained. “Tropical countries, especially in the Americas, are particularly susceptible due to their favorable climate for mosquito breeding.”
In early 2024, dengue infections in South America reached alarming levels, with over 670,000 cases reported in just the first five weeks of the year. This dramatic rise underscores the urgency of developing more effective predictive models that can account for various environmental and climate-related factors.
To enhance forecasting accuracy, Gabrick’s team turned to a machine learning approach using a “random forest” algorithm. This technique builds multiple decision trees that make predictions based on data the model has been trained on. “We selected random forest due to its robustness and the ability to consider various input factors simultaneously,” Gabrick explained. “By using this ensemble learning method, the model can evaluate the importance of different variables, offering crucial insights into what drives dengue outbreaks.”
The team tested the algorithm with historical data from dengue outbreaks in three tropical cities: Brazil, Peru, and Colombia. They created predictions based on three distinct sets of data: current dengue case numbers alone, case numbers combined with climate data (temperature, precipitation, and humidity), and case numbers with only humidity data.
The results were unexpected: each of the three variables produced the most accurate predictions in one of the cities, suggesting that the usefulness of climate data in dengue forecasting is not uniform. In some cases, climate data improved the model’s predictive capacity, while in others it did not provide additional benefits. This highlights the need for tailored approaches when combining climate and epidemiological data.
“The findings show that while climate variables like humidity can enhance predictive models, researchers must carefully consider how to integrate these factors for each specific location,” Gabrick noted. “Our study underscores the significance of using machine learning techniques to merge meteorological and epidemiological data, ultimately improving dengue forecasting and informing public health interventions.”
These advancements could provide crucial tools for health authorities to better predict and respond to dengue outbreaks, potentially saving thousands of lives in regions where the disease remains a major public health challenge.
For further details, refer to the study by Sidney T. da Silva et al., When Climate Variables Improve Dengue Forecasting: A Machine Learning Approach, The European Physical Journal Special Topics (2024). DOI: 10.1140/epjs/s11734-024-01201-7.
