AI becomes key weapon against dengue: fast diagnosis and outbreak prediction
Dengue is one of the most widespread mosquito-borne viral diseases on the planet. Every year it affects millions of people and causes health emergencies in Latin America, Asia, and increasingly in Europe. Faced with this challenge, artificial intelligence emerges as a powerful tool to anticipate risks, diagnose accurately, and save lives.
Algorithms and sensors to detect complications
A study led by Lyle et al. used to monitor blood volume in patients with severe dengue. Through machine learning algorithms, they were able to differentiate risk states with over 80% accuracy, which could prevent lethal complications such as hypovolemic shock.
On the other hand, Ho et al. developed neural network models and decision trees based on only four clinical variables (age, temperature, platelets, and leukocytes). These achieved up to 86% accuracy in diagnosing dengue, providing doctors with a quick and cost-effective tool.
Outbreak prediction with massive data
AI not only helps identify cases but also allows anticipating where and when the next outbreaks will occur. By combining demographic, human mobility, and surveillance data of the Aedes aegypti mosquito, algorithms generate dynamic risk maps.
In Brazil, systems like “M.I. Dengue” already use traps connected to mobile phones to monitor mosquito populations in real-time. This data is cross-referenced with health and climate information, allowing vector control campaigns to be activated days in advance.
A global challenge with local responses
in countries like Brazil, Paraguay, and Argentina, and in 2024 it also hit several regions in Europe. Faced with this scenario, AI offers adaptable solutions: from mobile applications to prediction platforms to guide healthcare logistics even in areas with limited resources.
In addition, bioinformatics studies have identified candidate genes such as STAT1 or BUB1 that could become biomarkers for early diagnosis or targeted therapies, opening up new lines of research.
What still needs to be addressed
Despite its advances, the widespread implementation of these tools faces barriers. The lack of real-time quality data, limited infrastructure in vulnerable regions, and the need for transparent and understandable models for medical personnel are the main challenges.
Experts insist that the key lies in collaboration among epidemiologists, AI specialists, entomologists, and health authorities. With investment in sensors, open data, and professional training, artificial intelligence could become a crucial ally against dengue and other diseases.
