Dengue Prediction

Predicting dengue fever outbreaks aims to improve public health interventions by providing timely warnings, enabling proactive resource allocation, and facilitating effective vector control strategies. Current research heavily utilizes machine learning, employing diverse architectures such as convolutional neural networks (CNNs), transformers (including variations like FWin transformers), and recurrent neural networks (RNNs, particularly LSTMs), often incorporating meteorological data, satellite imagery, entomological indices, and even incorporating correlations with other infectious diseases like COVID-19. These models aim to improve forecast accuracy and reliability across varying geographical contexts and data availability, with a particular focus on leveraging readily accessible data sources, such as satellite imagery, for resource-limited settings. The resulting improved prediction capabilities have significant implications for public health planning and resource management in dengue-endemic regions.