Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

Huang, Chiou-Jye; Shen, Yamin; Kuo, Ping‐Huan; Chen, Yung‐Hsiang

doi:10.1101/2020.04.30.20086538

Cited by 6 publications

(7 citation statements)

References 33 publications

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“…al. [1] proposed a novel deep learning model using convolutional and bidirectional GRU layers to forecast the virus in European countries. The study used the proposed architecture to process spatial and temporal features separately and obtained good performance.…”

Section: B Deep Learning Modelsmentioning

confidence: 99%

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

2020

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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 proposed model can both take in a large number of heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, past growth of infection, among others, and learn complex interactions between these features. Using data collected from various sources, we estimate the range of increase in infected cases seven days into the future for all U.S. counties. In addition, we use the model to identify the most influential features for prediction of the growth of infection. We also analyze pairs of features and estimate the amount of observed second-order interaction between them. Experiments show that the proposed model obtains satisfactory predictive performance and fairly interpretable feature analysis results; hence, the proposed model could complement the standard epidemiological models for nationallevel surveillance of pandemics, such as COVID-19. The results and findings obtained from the deep learning model could potentially inform policymakers and researchers in devising effective mitigation and response strategies. To fast-track further development and experimentation, the code used to implement the proposed model has been made fully open source.

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Section: B Deep Learning Modelsmentioning

confidence: 99%

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

2020

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“…These units are integral parts of convolutional neural networks (CNN), besides pooling and MLP units, and provide a methodology to extract local features from the data [66] , that have lesser dimensionality as compared to original data. This is achieved by the means of convolution with a kernel tensor, and provides a contrasting operation to standard tensor multiplication.…”

Section: Proposed Schemementioning

confidence: 99%

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model

Khan¹,

Ullah²,

Lee³

2022

Chaos, Solitons & Fractals

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“…Table 1: Used notations [32,30,8,7,13,27,28,23,9]. They use deep neural networks to extract complex temporal patterns from historical data and a selected set of additional features.…”

Section: [A; B]mentioning

confidence: 99%

Inter-Series Attention Model for COVID-19 Forecasting

Jin

Wang

Yan

2020

Preprint

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COVID-19 pandemic has an unprecedented impact all over the world since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resource allocation and administrative planning. The results from compartmental models such as SIR and SEIR are popularly referred by CDC and news media. With more and more COVID-19 data becoming available, we examine the following question: Can a direct data-driven approach without modeling the disease spreading dynamics outperform the well referred compartmental models and their variants? In this paper, we show the possibility. It is observed that as COVID-19 spreads at different speed and scale in different geographic regions, it is highly likely that similar progression patterns are shared among these regions within different time periods. This intuition lead us to develop a new neural forecasting model, called Attention Crossing Time Series (ACTS), that makes forecasts via comparing patterns across time series obtained from multiple regions. The attention mechanism originally developed for natural language processing can be leveraged and generalized to materialize this idea. Among 13 out of 18 testings including forecasting newly confirmed cases, hospitalizations and deaths, ACTS outperforms all the leading COVID-19 forecasters highlighted by CDC.

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Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

Cited by 6 publications

References 33 publications

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

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model

Inter-Series Attention Model for COVID-19 Forecasting

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