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Spatio-temporal prediction of the COVID-19 pandemic in US counties : modeling with a deep LSTM neural network
Title
Spatio-temporal prediction of the COVID-19 pandemic in US counties : modeling with a deep LSTM neural network
Author
Thill, Jean-Claude, Nikparvar, Behnam, Rahman, Md. Mokhlesur, Hatami, Faizeh
Date Created
2021
Subjects--Topical
Epidemiology, COVID-19 (Disease)
Description
Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand,...
Public perceptions of COVID-19 vaccines : policy implications from us spatiotemporal sentiment analytics
Title
Public perceptions of COVID-19 vaccines : policy implications from us spatiotemporal sentiment analytics
Author
Thill, Jean-Claude, Rahman, Md. Mokhlesur, Paul, Kamal Chandra
Date Created
2021
Subjects--Topical
Sentiment analysis, COVID-19 (Disease)
Description
There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of ...
Machine learning on the COVID-19 pandemic, human mobility and air quality : a review
Title
Machine learning on the COVID-19 pandemic, human mobility and air quality : a review
Author
Thill, Jean-Claude, Rahman, Md. Mokhlesur, Paul, Kamal Chandra
Date Created
2021
Subjects--Topical
Machine learning, COVID-19 (Disease)
Description
The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implem...
Machine learning of spatial data
Title
Machine learning of spatial data
Author
Thill, Jean-Claude, Nikparvar, Behnam
Date Created
2021
Subjects--Topical
Machine learning, Geospatial data
Description
Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to...
Exploring feasibility of multivariate deep learning models in predicting COVID-19 epidemic
Title
Exploring feasibility of multivariate deep learning models in predicting COVID-19 epidemic
Author
Thill, Jean-Claude, Janies, Daniel, Chen, Shi, Paul, Rajib
Date Created
2021
Subjects--Topical
Deep learning (Machine learning), COVID-19 (Disease)
Description
Background: Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate and comprehensive data. Complex epidemiological systems such as COVID-19 are especially...