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The COVID-19 pandemic has profound global consequences on health, economic,social, political, and almost every major aspect of human life. Therefore, itis of great importance to model COVID-19 and other pandemics in terms of thebroader social contexts in which they take place. We present the architectureof AICov, which provides an integrative deep learning framework for COVID-19forecasting with population covariates, some of which may serve as putativerisk factors. We have integrated multiple different strategies into AICov,including the ability to use deep learning strategies based on LSTM and evenmodeling. To demonstrate our approach, we have conducted a pilot thatintegrates population covariates from multiple sources. Thus, AICov not onlyincludes data on COVID-19 cases and deaths but, more importantly, thepopulation's socioeconomic, health and behavioral risk factors at a locallevel. The compiled data are fed into AICov, and thus we obtain improvedprediction by integration of the data to our model as compared to one that onlyuses case and death data.

AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates