An Explainable Multichannel Model for COVID-19 Time Series Prediction
- Authors: He H.1, Xie J.1, Lu X.1, Huang D.1, Zhang W.2
-
Affiliations:
- School of Computer Engineering and Science, Shanghai University
- College of Information Technology, Shanghai Jianqiao University
- Issue: Vol 19, No 7 (2024)
- Pages: 612-623
- Section: Life Sciences
- URL: https://jdigitaldiagnostics.com/1574-8936/article/view/643977
- DOI: https://doi.org/10.2174/1574893618666230727160507
- ID: 643977
Cite item
Full Text
Abstract
Introduction:The COVID-19 pandemic has affected every country and changed people's lives. Accurate prediction of COVID-19 trends can help prevent the further spread of the outbreak. However, the changing environment affects the COVID-19 prediction performance, and previous models are limited in practical applications.
Methods:An explainable multichannel deep learning model with spatial, temporal and environmental channels for time series prediction, STE-COVIDNet, was proposed. The time series data of COVID-19 infection, weather, in-state population mobility, and vaccination were collected from May, 2020, to October, 2021, in the USA. In the environmental channel of STE-COVIDNet, an attention mechanism was applied to extract significant environmental factors related to the spread of COVID-19. In addition, the attention weights of these factors were analyzed with the actual situation.
Results:STE-COVIDNet was found to be superior to other advanced prediction models of COVID-19 infection cases. The analysis results of attention weight were reported to be consistent with existing studies and reports. It was found that the same environmental factors that influence the spread of COVID-19 can vary across time and region, which explains why findings of previous studies on the relationship between the environment and COVID-19 vary by region and time.
Conclusion:STE-COVIDNet is an explainable model that can adapt to environmental changes and thus improve predictive performance.
About the authors
Hongjian He
School of Computer Engineering and Science, Shanghai University
Email: info@benthamscience.net
Jiang Xie
School of Computer Engineering and Science, Shanghai University
Author for correspondence.
Email: info@benthamscience.net
Xinwei Lu
School of Computer Engineering and Science, Shanghai University
Email: info@benthamscience.net
Dingkai Huang
School of Computer Engineering and Science, Shanghai University
Email: info@benthamscience.net
Wenjun Zhang
College of Information Technology, Shanghai Jianqiao University
Author for correspondence.
Email: info@benthamscience.net
References
- SanJuan-Reyes S, Gómez-Oliván LM, Islas-Flores H. COVID-19 in the environment. Chemosphere 2021; 263: 127973. doi: 10.1016/j.chemosphere.2020.127973 PMID: 32829224
- Hasan MB, Mahi M, Sarker T, Amin MR. Spillovers of the COVID-19 pandemic: Impact on global economic activity, the stock market, and the energy sector. Journal of Risk and Financial Management 2021; 14(5): 200. doi: 10.3390/jrfm14050200
- Cummings C, Dunkle J, Koller J, Lewis JB, Mooney L. Social work students and COVID-19: Impact across life domains. J Soc Work Educ 2023; 59(1): 91-103. doi: 10.1080/10437797.2021.1974992
- Hernandez-Matamoros A, Fujita H, Hayashi T, Perez-Meana H. Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Appl Soft Comput 2020; 96: 106610. doi: 10.1016/j.asoc.2020.106610 PMID: 32834798
- Quinlan JR. Combining instance-based and model-based learning. Proceedings of the tenth international conference on machine learning 236-43. doi: 10.1016/B978-1-55860-307-3.50037-X
- Breiman L. Random forests. Mach Learn 2001; 45(1): 5-32. doi: 10.1023/A:1010933404324
- Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970; 12(1): 55-67.https://www.tandfonline.com/doi/abs/10.1080/00401706.1970.10488634 doi: 10.1080/00401706.1970.10488634
- Drucker H, Burges CJ, Kaufman L, et al. Support vector regression machines. 1996. Available From: https://proceedings.neurips.cc/paper_files/paper/1996/file/d38901788c533e8286cb6400b40b386d-Paper.pdf
- Wolpert DH. Stacked generalization. Neural Netw 1992; 5(2): 241-59. doi: 10.1016/S0893-6080(05)80023-1
- Ribeiro MHDM, da Silva RG, Mariani VC, Coelho LS. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos Solitons Fractals 2020; 135: 109853. doi: 10.1016/j.chaos.2020.109853 PMID: 32501370
- He Y, Shen Z, Zhang Q, Wang S, Huang DS. A survey on deep learning in DNA/RNA motif mining. Brief Bioinform 2021; 22(4): bbaa229. doi: 10.1093/bib/bbaa229 PMID: 33005921
- Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017; 19(1): 221-48. doi: 10.1146/annurev-bioeng-071516-044442 PMID: 28301734
- Otter DW, Medina JR, Kalita JK. A survey of the usages of deep learning for natural language processing. IEEE Trans Neural Netw Learn Syst 2021; 32(2): 604-24. doi: 10.1109/TNNLS.2020.2979670 PMID: 32324570
- Huang Y, Zhang C, Lv X, et al. Survey of network intrusion detection based on deep learning. Journal of Information Security Research 2022; 8(12): 1163-77. http://www.sicris.cn/CN/Y2022/V8/I12/1163
- Gautam Y. Transfer Learning for COVID-19 cases and deaths forecast using LSTM network. ISA Trans 2022; 124: 41-56. doi: 10.1016/j.isatra.2020.12.057 PMID: 33422330
- Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons Fractals 2020; 140: 110212. doi: 10.1016/j.chaos.2020.110212 PMID: 32839642
- Shyam Sunder Reddy K, Padmanabha Reddy YCA, Mallikarjuna Rao C. WITHDRAWN: Recurrent neural network based prediction of number of COVID-19 cases in India. Mater Today Proc 2020; 1-4. doi: 10.1016/j.matpr.2020.11.117
- ArunKumar KE, Kalaga DV, Kumar CMS, Kawaji M, Brenza TM. Forecasting of COVID-19 using deep layer recurrent neural networks (RNNs) with gated recurrent units (GRUs) and long short-term memory (LSTM) cells. Chaos Solitons Fractals 2021; 146: 110861. doi: 10.1016/j.chaos.2021.110861 PMID: 33746373
- Alassafi MO, Jarrah M, Alotaibi R. Time series predicting of COVID-19 based on deep learning. Neurocomputing 2022; 468: 335-44. doi: 10.1016/j.neucom.2021.10.035 PMID: 34690432
- Kapoor A, Ben X, Liu L, et al. Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv 2020.
- Gao J, Sharma R, Qian C, et al. STAN: Spatio-temporal attention network for pandemic prediction using real-world evidence. J Am Med Inform Assoc 2021; 28(4): 733-43. doi: 10.1093/jamia/ocaa322 PMID: 33486527
- Yu Z, Zheng X, Yang Z, Lu B, Li X, Fu M. Interaction-temporal GCN: A hybrid deep framework for COVID-19 pandemic analysis. IEEE Open J Eng Med Biol 2021; 2: 97-103. doi: 10.1109/OJEMB.2021.3063890 PMID: 34812421
- Ioannidis JPA, Cripps S, Tanner MA. Forecasting for COVID-19 has failed. Int J Forecast 2022; 38(2): 423-38. doi: 10.1016/j.ijforecast.2020.08.004 PMID: 32863495
- Voysey M, Clemens SAC, Madhi SA, et al. Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: An interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK. Lancet 2021; 397(10269): 99-111. doi: 10.1016/S0140-6736(20)32661-1 PMID: 33306989
- Veličković P, Cucurull G, Casanova A, et al. Graph attention networks. arXiv 2017.
- McClymont H, Hu W. Weather variability and COVID-19 transmission: A review of recent research. Int J Environ Res Public Health 2021; 18(2): 396. doi: 10.3390/ijerph18020396 PMID: 33419216
- Tang KHD. Movement control as an effective measure against Covid-19 spread in Malaysia: An overview. Journal of Public Health 2020; 1-4. doi: 10.1007/s10389-020-01316-w PMID: 32837842
- Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. 2017. Available From: https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
- Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020; 20(5): 533-4. doi: 10.1016/S1473-3099(20)30120-1 PMID: 32087114
- Menne MJ, Durre I, Vose RS, Gleason BE, Houston TG. An overview of the global historical climatology network-daily database. J Atmos Ocean Technol 2012; 29(7): 897-910. doi: 10.1175/JTECH-D-11-00103.1
- Ritchie H, Mathieu E, Rodés-Guirao L, et al. 2020. Available From: https://ourworldindata.org/coronavirus
- Reid S, Nicolis O, Peralta B. Predicting the COVID-19 in the Metropolitan Region (Chile) using a GCN-LSTM neural network. 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON). In: IEEE. 2021; pp. 1-6. doi: 10.1109/CHILECON54041.2021.9702969
- Yang W. Modeling COVID-19 pandemic with hierarchical quarantine and time delay. Dyn Games Appl 2021; 11(4): 892-914. doi: 10.1007/s13235-021-00382-3 PMID: 33777480
- Tobías A, Molina T. Is temperature reducing the transmission of COVID-19? Environ Res 2020; 186: 109553. doi: 10.1016/j.envres.2020.109553 PMID: 32330766
- Chien LC, Chen LW. Meteorological impacts on the incidence of COVID-19 in the U.S. Stochastic Environ Res Risk Assess 2020; 34(10): 1675-80. doi: 10.1007/s00477-020-01835-8 PMID: 32837311
- Chan AY, Kim H, Bell ML. Higher incidence of novel coronavirus (COVID-19) cases in areas with combined sewer systems, heavy precipitation, and high percentages of impervious surfaces. Sci Total Environ 2022; 820: 153227. doi: 10.1016/j.scitotenv.2022.153227 PMID: 35051454
- Menebo MM. Temperature and precipitation associate with Covid-19 new daily cases: A correlation study between weather and Covid-19 pandemic in Oslo, Norway. Sci Total Environ 2020; 737: 139659. doi: 10.1016/j.scitotenv.2020.139659 PMID: 32492607
- Bukhari Q, Massaro JM, DAgostino RB Sr, Khan S. Effects of weather on coronavirus pandemic. Int J Environ Res Public Health 2020; 17(15): 5399. doi: 10.3390/ijerph17155399 PMID: 32727067
- Storr VH, Haeffele S, Lofthouse JK, Grube LE. Essential or not? Knowledge problems and COVID ‐19 stay‐at‐home orders. South Econ J 2021; 87(4): 1229-49. doi: 10.1002/soej.12491 PMID: 33821051
- Gumel AB, Iboi EA, Ngonghala CN, Ngwa GA. Toward achieving a vaccine-derived herd immunity threshold for COVID-19 in the US. Front Public Health 2021; 9: 709369. doi: 10.3389/fpubh.2021.709369 PMID: 34368071
Supplementary files
