DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions

DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions

In this paper, we propose a deep learning model to forecast the range of increase in COVID-19 infected cases in future days and we present a novel method to compute equidimensional representations of multivariate time series and multivariate spatial time series data. Using this novel method, the pro...

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Bibliographic Details
Main Authors: Ankit Ramchandani, Chao Fan, Ali Mostafavi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
COVID-19
deep learning
interpretable machine learning
feature interactions
pandemic surveillance
disease spread modeling
Online Access:https://ieeexplore.ieee.org/document/9179729/

Internet

https://ieeexplore.ieee.org/document/9179729/

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