AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates
Volume 19, Issue 2 (2021), pp. 293–313
Pub. online: 22 February 2021 Type: Computing In Data Science
1 July 2020
1 July 2020
23 January 2021
23 January 2021
22 February 2021
22 February 2021
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 past data.
Supplementary materialSupplementary Material
The code and paper document represented to implement AICov are contained in several repositories: 1. The entire cloudmesh code on which the cloud based implementation of the AICov framework is based and contains over 70 contributors is available publicly at https://github.com/cloudmesh. Cloudmesh contains a number of modules that dependent on the users access to cloud resources can be customized. A detailed manual about the configuration is available at https://cloudmesh.github.io/cloudmesh-manual/. 2. The entire COVID-19 analysis leverages cloudmesh and uses Jupyter notebooks to coordinate its workflow as discussed in the architecture Figure 2. The code and data for the results presented in this paper are located in the repository at https://github.com/cloudmesh/cloudmesh-covid. The data was analysed on a variety of supercomputing resources including an allocation of 20 compute nodes that were utilized to execute the repeated model creation to assure reproducible results. However, the use of the data is copyrighted and must be authorized to be used for other publications without contacting the authors. The data gathering and analysis is a significant intellectual contribution and we like to avoid that the data is taken before we have not secured a publication. 3. The entire paper is located in LaTeXsource in the GitHub repository https://github.com/cyberaide/paper-covid. This repository will be open sourced after acceptance of publication to not violate any publisher restrictions. If desired the authors can grant access to this repository prior to publication. Please contact the corresponding author. A zip file is provided for the publication for archival purposes. However, it will be much more convenient and easier to use our GitHub distribution as discussed in the supplementary section.
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