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Influenza-like illness (ILI) places a heavy social and economic burden on oursociety. Traditionally, ILI surveillance data is updated weekly and provided ata spatially coarse resolution. Producing timely and reliable high-resolutionspatiotemporal forecasts for ILI is crucial for local preparedness and optimalinterventions. We present TDEFSI (Theory Guided Deep Learning Based EpidemicForecasting with Synthetic Information), an epidemic forecasting framework thatintegrates the strengths of deep neural networks and high-resolutionsimulations of epidemic processes over networks. TDEFSI yields accuratehigh-resolution spatiotemporal forecasts using low-resolution time series data.During the training phase, TDEFSI uses high-resolution simulations of epidemicsthat explicitly model spatial and social heterogeneity inherent in urbanregions as one component of training data. We train a two-branch recurrentneural network model to take both within-season and between-seasonlow-resolution observations as features, and output high-resolution detailedforecasts. The resulting forecasts are not just driven by observed data butalso capture the intricate social, demographic and geographic attributes ofspecific urban regions and mathematical theories of disease propagation overnetworks. We focus on forecasting the incidence of ILI and evaluate TDEFSI'sperformance using synthetic and real-world testing datasets at the state andcounty levels in the USA. The results show that, at the state level, our methodachieves comparable/better performance than several state-of-the-art methods.At the county level, TDEFSI outperforms the other methods. The proposed methodcan be applied to other infectious diseases as well.

TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information