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Title: AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates
The COVID-19 (COrona VIrus Disease 2019) pandemic has had profound global consequences on health, economic, social, behavioral, 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 an artificial intelligence enhanced COVID-19 analysis (in short 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 Long Short-Term Memory (LSTM) and event modeling. To demonstrate our approach, we have introduced a framework 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 their specific locations. The compiled data are fed into AICov, and thus we obtain improved prediction by the integration of the data to our model as compared to one that only uses case and death data. As we use deep learning our models adapt over time while learning the model from more » past data. « less
Authors:
; ; ;
Award ID(s):
1829704 1918626 1835631 1443054 2151597
Publication Date:
NSF-PAR ID:
10287098
Journal Name:
Journal of Data Science
Volume:
19
Issue:
2
Page Range or eLocation-ID:
293 to 313
ISSN:
1680-743X
Sponsoring Org:
National Science Foundation
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