Modelling climatic and temporal dynamics of dengue transmission in Bangladesh using deep learning models

Comparative Performance of LSTM and SARIMAX for Monthly Dengue Fever Forecasting in Sukabumi City

Findings indicate that SARIMAX is more suitable for forecasting dengue incidence characterized by strong seasonal patterns and relatively limited observations, than LSTM.

Climate-Aware LSTM Framework for Dengue Forecasting: Evidence from Multi-City Indian and SJ/IQ Datasets

The experimental results show that climate-aware LSTM models are good at predicting dengue cases and can help build strong early warning systems in areas where cases are dependent on climate change.

Prognosis of vector borne dengue disease outbreak in urban areas using multivariate analysis

The feasibility of predicting dengue outbreaks using weekly multivariate time-series data from San Juan, Puerto Rico and Iquitos, Peru is examined, demonstrating that advanced machine learning models can serve as reliable early warning systems for vector-borne diseases.

Comparative evaluation of machine learning strategies for short-term dengue forecasting in Brazilian capital municipalities

Dengue is an arboviral disease of high public health relevance, characterized by pronounced temporal variability, nonlinearity, and recurrent outbreaks, which pose challenges to epidemiological surveillance and decision-making. This study evaluated the performance of machine learning methods for short-term forecasting of the weekly dengue morbidity rate in the 27 Brazilian capital cities, comprising the 26 state capitals and Brasília, Federal District, with horizons up to 4 weeks. Epidemiological, climatic, and socioeconomic data were compiled for these capital cities and used to compare a Gated Recurrent Unit (GRU) neural network, formulated as a Multi-Input Multi-Output (MIMO) model, and a Gradient Boosting model (CatBoost), implemented using a Direct forecasting strategy with horizon-specific models. Validation was conducted using a walk-forward approach, with evaluation based on absolute error metrics and the coefficient of determination. The results indicated that the GRU architecture presented recurring limitations, including underfitting, temporal lag, and low capacity to anticipate epidemic peaks. In contrast, the CatBoost model demonstrated greater robustness and better adaptation to the variability of epidemiological time series, showing superior performance in most of the analyzed capitals. The findings reinforce that greater architectural complexity does not necessarily imply better operational performance and highlight the potential of ensemble-based methods for short-term epidemiological surveillance applications. These findings contribute to dengue forecasting by showing that, under a common validation framework, ensemble-based strategies may provide greater operational robustness than recurrent MIMO architectures for short-term prediction in heterogeneous epidemiological settings.

Explainable Artificial Intelligence (XAI) for predicting Dengue severity using Temporal Convolution Network and Multi-modal Adaptive Fusion

A spatiotemporal U-Net++ deep learning framework for dengue risk mapping in Colombia

Understanding dengue dynamics from a spatiotemporal perspective has become increasingly relevant in recent years, as it allows the identification of factors influencing disease transmission, including climatic conditions, human behavior, and the distribution of Aedes aegypti, the primary vector responsible for dengue virus transmission in tropical and subtropical regions. This knowledge is essential for supporting public health decision-making and reducing the impact of dengue on vulnerable populations. This study proposes a spatio-temporal deep learning framework based on the U-Net++ architecture to generate high-resolution dengue risk maps in Colombia. The model integrates climatic, environmental, demographic, and socioeconomic information derived from satellite and census sources. Two spatial approaches were evaluated: a high-dimensional geography (HDG) approach at the national scale and a low-dimensional geography (LDG) approach applied to five departments with high dengue incidence. The model integrating climatic, social, and environmental information achieved the best performance in both approaches. In the HDG configuration, the best model (M9) reached a test mIoU of 0.6646 (validation mIoU = 0.7361). In the LDG configuration, the corresponding model achieved an average test mIoU of approximately 0.73 across departments. These results highlight the potential of integrating heterogeneous climatic, environmental, and socioeconomic data within a spatiotemporal deep learning framework to characterize dengue risk patterns and support high-resolution surveillance.