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In this paper, we propose a deep learning model to forecast the range ofincrease in COVID-19 infected cases in future days and we present a novelmethod to compute equidimensional representations of multivariate time seriesand multivariate spatial time series data. Using this novel method, theproposed model can both take in a large number of heterogeneous features, suchas census data, intra-county mobility, inter-county mobility, social distancingdata, past growth of infection, among others, and learn complex interactionsbetween these features. Using data collected from various sources, we estimatethe range of increase in infected cases seven days into the future for all U.S.counties. In addition, we use the model to identify the most influentialfeatures for prediction of the growth of infection. We also analyze pairs offeatures and estimate the amount of observed second-order interaction betweenthem. Experiments show that the proposed model obtains satisfactory predictiveperformance and fairly interpretable feature analysis results; hence, theproposed model could complement the standard epidemiological models fornational-level surveillance of pandemics, such as COVID-19. The results andfindings obtained from the deep learning model could potentially informpolicymakers and researchers in devising effective mitigation and responsestrategies. To fast-track further development and experimentation, the codeused to implement the proposed model has been made fully open source.

DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their Interactions