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

Deep learning with multiscale spatial context improves global dengue suitability mapping

Infectious-disease risk models often rely on occurrence records that are incomplete and spatially biased by surveillance effort, diagnostic access, and outbreak history. Ecological niche modelling (ENM) can identify areas where disease occurrence is environmentally plausible, yet most approaches represent locations using only pointwise covariate values and therefore overlook the surrounding spatial context and rely on presence-only data. Here, we present a deep-learning framework for presence-only data that estimates relative disease suitability by comparing the environmental conditions surrounding reported occurrences with those sampled across the wider study area. The model processes gridded environmental patches at local, neighbourhood, and broader landscape scales, learns the contribution of each scale, and accommodates missing raster values. Using dengue virus as a global case study, we evaluate whether multiscale spatial representation improves upon point-based ENM baselines including random forest and maximum entropy (MaxEnt) under a spatially disjoint train-test design. The model achieved a Boyce index of 0.971 and an AUC of 0.976 on the held-out test set. Learned scale weights and ablation experiments indicated that neighbourhood context contributed most strongly, while local and broader-scale information provided complementary predictive signals. Compared with point-based baselines, the model identified 6-18% more environmentally suitable area across South Asia, Southeast Asia, and South America, encompassing tens of millions of residents. These findings demonstrate that multiscale spatial context can improve estimates of relative dengue suitability. More broadly, mask-aware convolutional density-ratio estimation provides a flexible framework for mapping environmentally structured pathogens from incomplete, presence-only occurrence data.

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.

Data-Driven Geospatial Modeling and Forecasting of Malaria Burden to Support Control and Elimination in Africa

Malaria elimination is shaped by complex interactions among climatic, environmental, socioeconomic, demographic, health-system, and intervention-related factors. However most studies examine only subsets of these drivers, limiting understanding of their combined influence on epidemiological risks. In this study, we integrated 25 years of data from 44 African countries on malaria burden and control, climate, environmental and land-use conditions, socioeconomic and demographic characteristics, and health-system capacity within a unified geospatial, explainable machine-learning, and forecasting framework to characterize spatiotemporal patterns of malaria, quantify the relative contributions of key determinants, and generate 10-year Africa-wide and country-specific forecasts of malaria incidence and mortality rates per 1,000 people at risk. We identified and mapped malaria incidence and mortality hotspots using the Getis-Ord Gi* statistic. Our analyses showed that both incidence and mortality burden remained highly heterogeneous across Africa, with persistent hotspots concentrated in the West and Central Africa. The explainable machine-learning model, that achieved high predictive performance (i.e., XGBoost for incidence, holdout R$^2$ = 0.92; Random Forest for mortality, holdout R$^2$ = 0.91), identified lower availability of hospital beds (per 1,000 people), higher mortality rate attributed to unsafe WASH (per 100,000), and lower percentage (%) of people using handwashing facilities as the top three most influential determinants of higher risk of infection across Africa whereas higher mortality rate attributed to unsafe WASH (per 100,000), lower % of people using at least basic sanitation services, and access to electricity (%) were associated with worse mortality outcomes. Forecasting models also demonstrated strong predictive accuracy (Naive persistence and Elastic Net, holdout R$^2$ = 0.98 for incidence and 0.97 for mortality). Assuming current intervention and structural conditions persist, Africa-wide malaria incidence was projected to remain broadly stable, with a modest upward trend by 2035, whereas mortality was projected to decline initially and subsequently remain relatively unchanged. However, substantial country-level heterogeneity showed both emerging transmission hotspots and persistently high-burden countries. this suggests there is a need for sustained control and accelerated elimination efforts. Overall, this study demonstrates that integrating geospatial analysis, explainable machine learning, and forecasting provides a robust framework for understanding malaria dynamics, identifying the key determinants of burden, anticipating future trends, and supporting geographically targeted malaria control across Africa. Beyond malaria, our analyses can be applied as a generalizable approach for infectious disease surveillance, early-warning systems, hotspot detection, resource prioritization, and precision public health using large-scale longitudinal health data.

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

Geographically Weighted Machine Learning Model for Untangling Spatial Heterogeneity of Dengue Incidence in West Java

The results imply that the GWRF is more effective in modeling dengue fever outbreaks and can inform the development of region-specific public health interventions.