Abstract
Disease forecasting is a longstanding problem for the research
community, which aims at informing and improving decisions with the best
available evidence. Specifically, the interest in respiratory disease
forecasting has dramatically increased since the beginning of the
coronavirus pandemic, rendering the accurate prediction of
influenza-like-illness (ILI) a critical task. Although  methods for
short-term ILI forecasting and nowcasting have achieved good accuracy,
their performance worsens at long-term ILI forecasts. Machine learning
models have outperformed conventional forecasting approaches enabling to
utilize diverse exogenous data sources, such as social media, internet
users’ search query logs, and climate data. However, the most recent
deep learning ILI forecasting models use only historical occurrence data
achieving state-of-the-art results. Inspired by recent deep neural
network architectures in time series forecasting that benefit from
self-attention, this work proposes the Regional Influenza-Like-Illness
Forecasting (ReILIF) method for regional long-term ILI prediction. The
proposed architecture takes advantage of diverse exogenous data, that
are, meteorological and population data, introducing an efficient
intermediate fusion mechanism to combine the different types of
information with the aim to capture the variations of ILI from various
views. The efficacy of the proposed approach compared to
state-of-the-art ILI forecasting methods is confirmed by an extensive
experimental study following standard evaluation measures.