Multi-model approach to understand and predict past and future dengue epidemic dynamics

Understanding the past, current, and future dynamics of dengue epidemics is challenging yet increasingly important for global public health. Using data from northern Peru across 2010 -- 2021, we introduce a multi-model approach that integrates new and existing techniques for understanding and predicting dengue epidemics. Using wavelet analyses, we unveil spatiotemporal patterns and estimate space-varying epidemic drivers across shorter and longer dengue cycles, while our Bayesian hierarchical model allows us to quantify the timing, structure, and intensity of such climatic influences. For forecasting, as a single model is generally sub-optimal, we introduce trained and untrained probabilistic ensembles. In settings that mirror real-world implementations, we develop climate-informed and covariate-free deep learning forecasting models involving foundational time series, temporal convolutional networks, and conformal inference. We complement modern techniques with statistically principled training, assessment, and benchmarking of ensembles, alongside interpretable metrics for outbreak detection to disseminate outputs with communities and public health authorities. Our ensembles generally outperformed individual models across space and time. Looking forward, whether the public health objective is to learn from the past and/or to predict future dengue epidemic dynamics, our multi-model approach can be used to inform the decision-making of public health authorities.