Quantum Computing for Dengue Fever Outbreak Prediction

Abstract

Dengue virus infection originates from the Aedes mosquito species. The authors propose a novel paradigm to revolutionize dengue fever detection and recommendation systems by leveraging the potential of quantum computing. Using meteorological data and past dengue cases, they create a prediction framework that goes beyond traditional constraints. Quantum machine learning methods are proposed for discovering hidden patterns within enormous datasets, allowing them to identify detailed relationships between environmental conditions and illness occurrences. Traditional machine learning algorithms are all part of our strategy. Quantum optimization techniques further optimize these models, enhancing predictive accuracy while minimizing resource consumption. As we navigate challenges such as data integrity, model validation, and quantum hardware constraints, interdisciplinary collaboration between epidemiologists, quantum scientists, and healthcare experts becomes paramount. The analytical results from data show improvement in more cases of dengue prediction in the various districts of Tamil Nadu.