Geospatial-based surveillance of malaria risk in Dar es Salaam using a hybrid 3DCNN + LSTM and CA model

Malaria remains a significant public health burden in tropical and subtropical regions, where the efficient identification and prediction of risk areas remain challenging. Conventional field surveys used to map Anopheles breeding sites are costly, time consuming and often incomplete. Therefore, there is a pressing need for a geospatially integrated surveillance framework that can accurately map malaria risk and forecast future risk dynamics to support targeted control efforts. A geospatial hybrid modelling framework was developed by integrating multi-source remote sensing, malaria, and climate datasets. A Random Forest model was employed to determine the relative importance of the input variables, which were then weighted and selected for inclusion in a deep-learning architecture. The predictive model combines a 3D Convolutional Neural Network (3DCNN) to capture spatial patterns with a Long Short-Term Memory (LSTM) network to learn temporal dynamics. The model was trained against a baseline mean squared error (MSE) of 0.1 representing a naïve mean predictor. To improve spatial realism of the final risk maps, a Cellular Automata (CA) model was incorporated using a 3 × 3 Moore neighborhood structure, with parameters calibrated at γ = 0.293 and β = 43.9 to enhance the spatial propagation of risk across neighboring cells. The framework successfully mapped malaria risk in Dar es Salaam with a Spearman rank correlation of 0.92, identifying Kigamboni South, Tundwi, and Msongola as high-risk areas. The hybrid 3DCNN-LSTM model reduced the training by 97.3% and validation losses by 90.2% respectively from the baseline. Projections to 2060 indicate a steadily spatial increase in malaria risk, with a cumulative slope of 0.077 risk units over the 35-year horizon (an annual slope of 0.002), demonstrating the model's ability to estimate future risk.