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