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The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to mitigate the risk and spread of diseases on their college campus. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, environmental and behavioral risk factors, disease progression, and control interventions. The contribution of mitigation strategies to disease transmission was explored without and with interventions such as vaccination, quarantine of symptomatic cases, and testing. We show that even with high vaccination coverage (90%) college campuses may still experience sizable outbreaks. The size of the outbreaks varies with the underlying environmental and socio-behavioral risk factors. Complementing vaccination with quarantine and mass testing was shown to be paramount for preventing or mitigating outbreaks. Though our quantitative results are likely provisional on our model assumptions, sensitivity analysis confirms the robustness of their qualitative nature.
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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 past data.
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- PAR ID:
- 10287098
- Date Published:
- Journal Name:
- Journal of Data Science
- Volume:
- 19
- Issue:
- 2
- ISSN:
- 1680-743X
- Page Range / eLocation ID:
- 293 to 313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.6339/21-JDS1007
