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1902.05113
Cited By

A Study on Graph-Structured Recurrent Neural Networks and Sparsification
with Application to Epidemic Forecasting

A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting

World Congress on Global Optimization (WCGO), 2019

13 February 2019

Andrea L. Bertozzi

ArXiv (abs)PDF HTML

Papers citing "A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting"

4 / 4 papers shown

An integrated recurrent neural network and regression model with spatial
and climatic couplings for vector-borne disease dynamics

An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamicsInternational Conference on Pattern Recognition Applications and Methods (ICPRAM), 2022

94

1

0

23 Jan 2022

A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with
Application to COVID-19

A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2020

151

12

0

18 Oct 2020

A Recurrent Neural Network and Differential Equation Based
Spatiotemporal Infectious Disease Model with Application to COVID-19

A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19medRxiv (medRxiv), 2020

146

14

0

14 Jul 2020

Towards the Use of Neural Networks for Influenza Prediction at Multiple
Spatial Resolutions

Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions

Mauricio Santillana

93

15

0

06 Nov 2019

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