HierarNet: Independent Interactive Hierarchical Disease Outbreak Forecasting

Early warning systems for disease outbreaks play a crucial role in public health for management and contingency planning. However, most predictive modeling works focus on flat models that incorporate exogenous inputs (e.g. climate, demographics) to predict future outbreaks at different locations, but do not jointly model multiple spatial aggregation levels. In this paper, we introduce \emph{HierarNet}, a unique \emph{independent-interactive hierarchical} forecasting framework that aims to predict disease outbreaks at different levels of spatial resolution, such as provinces, regions, and nations. HierarNet consists of two main phases. In the \emph{local} phase, we train \emph{independent} forecasting models for all locations at all levels. In the \emph{global} phase, all models \emph{iteratively interact} with others across different levels via their hierarchical relationships under an \emph{ensemble} fashion to maximize their agreements. This \emph{global local hierarchical interactive} scheme makes HierarNet a highly effective and \emph{flexible} method (i.e. it can work with an arbitrary base prediction model and available exogenous data for each location independently). Extensive experiments are conducted on various disease datasets (e.g., Dengue fever, flu, diarrhea, and Bluetongue) in different countries (e.g., France, Vietnam, and USA) to show the performance of HierarNet compared to 19 state-of-the-art (SOTA) methods such as MinT, DYCHEM, WITRAN, SegRNN, TSMixer, PatchTST, or iTransformer. We also illustrate the \emph{generability} of HierarNet in other domains, e.g., web traffic forecasting.