Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information

Author:

Roy Padmaksha1,Sarkar Shailik1,Biswas Subhodip1,Chen Fanglan1,Chen Zhiqian2,Ramakrishnan Naren1,Lu Chang-Tien1

Affiliation:

1. Virginia Tech

2. Mississippi State University

Funder

National Science Foundation

Publisher

ACM

Reference31 articles.

1. K. Nikolopoulos , S. Punia , A. Schäfers , C. Tsinopoulos , and C. Vasilakis , " Forecasting and planning during a pandemic: Covid-19 growth rates, supply chain disruptions, and governmental decisions," European journal of operational research , vol. 290 , no. 1, pp. 99--115, 2021. K. Nikolopoulos, S. Punia, A. Schäfers, C. Tsinopoulos, and C. Vasilakis, "Forecasting and planning during a pandemic: Covid-19 growth rates, supply chain disruptions, and governmental decisions," European journal of operational research, vol. 290, no. 1, pp. 99--115, 2021.

2. A SIR model assumption for the spread of COVID-19 in different communities

3. Spatial prediction of COVID-19 epidemic using ARIMA techniques in India;Roy S.;Modeling Earth Systems and Environment,2020

4. P. Melin , J. C. Monica , D. Sanchez , and O. Castillo , " Multiple ensemble neural network models with fuzzy response aggregation for predicting COVID-19 time series: The case of Mexico," Healthcare (Basel) , vol. 8 , no. 2, Jun. 2020. P. Melin, J. C. Monica, D. Sanchez, and O. Castillo, "Multiple ensemble neural network models with fuzzy response aggregation for predicting COVID-19 time series: The case of Mexico," Healthcare (Basel), vol. 8, no. 2, Jun. 2020.

5. M. Kim , J. Kang , D. Kim , H. Song , H. Min , Y. Nam , D. Park , and J.-G. Lee , "Hi-COVIDNet : Deep learning approach to predict inbound COVID-19 patients and case study in south korea," in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020 , pp. 3466 -- 3473 . M. Kim, J. Kang, D. Kim, H. Song, H. Min, Y. Nam, D. Park, and J.-G. Lee, "Hi-COVIDNet: Deep learning approach to predict inbound COVID-19 patients and case study in south korea," in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 3466--3473.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MTSNet: Deep Probabilistic Cross-multivariate Time Series Modeling with External Factors for COVID-19;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

2. Explainable Prediction of the Severity of COVID-19 Outbreak for US Counties;2022 IEEE International Conference on Big Data (Big Data);2022-12-17

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