Neural-SEIR: A flexible data-driven framework for precise prediction of epidemic disease

Haoyu Wang; Xihe Qiu; Jinghan Yang; Qiong Li; Xiaoyu Tan; Jingjing Huang; Haoyu Wang; Xihe Qiu; Jinghan Yang; Qiong Li; Xiaoyu Tan; Jingjing Huang

doi:10.3934/mbe.2023749

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 16807-16823. doi: 10.3934/mbe.2023749

Previous Article Next Article

Research article

Neural-SEIR: A flexible data-driven framework for precise prediction of epidemic disease

1.
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China
3.
INF Technology (Shanghai) Co., Ltd., Shanghai 201203, China
4.
Department of Otolaryngology-Head and Neck Surgery, Eye & ENT Hospital of Fudan University, Shanghai 200031, China
5.
Sleep Disordered Medical Center, Shanghai Municipal Key Clinical Specialty, China

Received: 18 July 2023 Revised: 12 August 2023 Accepted: 12 August 2023 Published: 23 August 2023

Accurately modeling and predicting epidemic diseases is crucial to prevent disease transmission and reduce mortality. Due to various unpredictable factors, including population migration, vaccination, control efforts, and seasonal fluctuations, traditional epidemic models that rely on prior knowledge of virus transmission mechanisms may not be sufficient to forecast complex epidemics like coronavirus disease 2019(COVID-19). The application of traditional epidemiological models such as susceptible-exposed-infectious-recovered (SEIR) may face difficulties in accurately predicting such complex epidemics. Data-driven prediction approaches lack the ability to generalize and exhibit low accuracy on small datasets due to their reliance on large amounts of data without incorporating prior knowledge. To overcome this limitation, we introduce a flexible ensemble data-driven framework (Neural-SEIR) that "neuralizes" the SEIR model by approximating the core parameters through neural networks while preserving the propagation structure of SEIR. Neural-SEIR employs long short-term memory (LSTM) neural network to capture complex correlation features, exponential smoothing (ES) to model seasonal information, and prior knowledge from SEIR. By incorporating SEIR parameters into the neural network structure, Neural-SEIR leverages prior knowledge while updating parameters with real-world data. Our experimental results demonstrate that Neural-SEIR outperforms traditional machine learning and epidemiological models, achieving high prediction accuracy and efficiency in forecasting epidemic diseases.
- Neural-SEIR,
- long short-term memory,
- infectious disease prediction,
- time-series data
Citation: Haoyu Wang, Xihe Qiu, Jinghan Yang, Qiong Li, Xiaoyu Tan, Jingjing Huang. Neural-SEIR: A flexible data-driven framework for precise prediction of epidemic disease[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16807-16823. doi: 10.3934/mbe.2023749

Related Papers:

Abstract

Accurately modeling and predicting epidemic diseases is crucial to prevent disease transmission and reduce mortality. Due to various unpredictable factors, including population migration, vaccination, control efforts, and seasonal fluctuations, traditional epidemic models that rely on prior knowledge of virus transmission mechanisms may not be sufficient to forecast complex epidemics like coronavirus disease 2019(COVID-19). The application of traditional epidemiological models such as susceptible-exposed-infectious-recovered (SEIR) may face difficulties in accurately predicting such complex epidemics. Data-driven prediction approaches lack the ability to generalize and exhibit low accuracy on small datasets due to their reliance on large amounts of data without incorporating prior knowledge. To overcome this limitation, we introduce a flexible ensemble data-driven framework (Neural-SEIR) that "neuralizes" the SEIR model by approximating the core parameters through neural networks while preserving the propagation structure of SEIR. Neural-SEIR employs long short-term memory (LSTM) neural network to capture complex correlation features, exponential smoothing (ES) to model seasonal information, and prior knowledge from SEIR. By incorporating SEIR parameters into the neural network structure, Neural-SEIR leverages prior knowledge while updating parameters with real-world data. Our experimental results demonstrate that Neural-SEIR outperforms traditional machine learning and epidemiological models, achieving high prediction accuracy and efficiency in forecasting epidemic diseases.

References

[1]	R. Sabino-Silva, A.C.G. Jardim, W.L. Siqueira, Coronavirus covid-19 impacts to dentistry and potential salivary diagnosis, Clin. Oral. Invest, 24 (2020), 1619–1621. https://doi.org/10.1007/s00784-020-03248-x doi: 10.1007/s00784-020-03248-x
[2]	X. Wang, Z. Wang, H. Shen, Dynamical analysis of a discrete-time sis epidemic model on complex networks, Appl. Math. Lett, 94 (2019), 292–299. https://doi.org/10.1016/j.aml.2019.03.011 doi: 10.1016/j.aml.2019.03.011
[3]	X. Meng, S. Zhao, T. Feng, T. Zhang, Dynamics of a novel nonlinear stochastic sis epidemic model with double epidemic hypothesis, J. Math. Anal. Appl., 7 (2015), 227–242. https://doi.org/10.1016/j.jmaa.2015.07.056 doi: 10.1016/j.jmaa.2015.07.056
[4]	N. Sene, Sir epidemic model with mittag–leffler fractional derivative, Chaos. Soliton. Fract., 137 (2020). https://doi.org/10.1016/j.chaos.2020.109833 doi: 10.1016/j.chaos.2020.109833
[5]	D. Courtney, P. Watson, M. Battaglia, B. H. Mulsant, P. Szatmari, Covid-19 impacts on child and youth anxiety and depression: Challenges and opportunities, Can. J. Psych., 10 (2020). https://doi.org/10.1177/0706743720935646 doi: 10.1177/0706743720935646
[6]	P. Khanna, S. Kumar, Malaria parasite classification employing chan–vese algorithm and svm for healthcare, IC4S, (2019), 697-711. https://doi.org/10.1007/978-981-15-3369-3_51 doi: 10.1007/978-981-15-3369-3_51
[7]	J. W. Tian, Y. Liu, W. F. Zheng, L. R. Yin, Smog prediction based on the deep belief - BP neural network model (DBN-BP), Urban. Clim., 41 (2022). https://doi.org/10.1016/j.uclim.2021.101078 doi: 10.1016/j.uclim.2021.101078
[8]	M. O. Edeh, S. Dalal, I. C.Obagbuwa, B. V. V. S. Prasad, S. Z. Ninoria, M. A. Wajid, et al., Bootstrapping random forest and chaid for prediction of white spot disease among shrimp farmers, SCI. Rep.-UK. https://doi.org/10.1038/s41598-022-25109-1
[9]	C. J. Huang, Y. H. Chen, Y. Ma, P. H. Kuo, Multiple-input deep convolutional neural network model for covid-19 forecasting in china, MedRxiv (2020). https://doi.org/10.1101/2020.03.23.20041608 doi: 10.1101/2020.03.23.20041608
[10]	V. K. R. Chimmula, L. Zhang, Time series forecasting of covid-19 transmission in canada using lstm networks, Chaos. Soliton. Fract., 135 (2020), 109864. https://doi.org/10.1016/j.chaos.2020.109864 doi: 10.1016/j.chaos.2020.109864
[11]	S. Ketu, P. K. Mishra, India perspective: Cnn-lstm hybrid deep learning model-based covid-19 prediction and current status of medical resource availability, Soft. Comput., 26 (2022), 645–-664. https://doi.org/10.1007/s00500-021-06490-x doi: 10.1007/s00500-021-06490-x
[12]	Q. Ni, J. Kang, M. Tang, Y. Liu, Y. Zou, Learning epidemic threshold in complex networks by convolutional neural network, Chaos, 29 (2019), 113106. https://doi.org/10.1063/1.5121401 doi: 10.1063/1.5121401
[13]	S. Jafarizadeh, D. Veitch, Optimal curing resource allocation for epidemic spreading processes. Automatica, 150 (2023), 110851. https://doi.org/10.1016/j.automatica.2023.110851 doi: 10.1016/j.automatica.2023.110851
[14]	R. Engbert, M. M. Rabe, R. Kliegl, S. Reich, Sequential data assimilation of the stochastic seir epidemic model for regional covid-19 dynamics, B. Math. Biol., 83 (2021). https://doi.org/10.1007/s11538-020-00834-8 doi: 10.1007/s11538-020-00834-8
[15]	N. S. Barlow, S. J. Weinstein, Corrigendum to "accurate closed-form solution of the sir epidemic model" [physica d 408 (2020) 132540], PHYSICA. D, 416 (2020), 132807. https://doi.org/10.1016/j.physd.2020.132540 doi: 10.1016/j.physd.2020.132540
[16]	K. M. A. Kabir, K. Kuga, J. Tanimoto, Analysis of sir epidemic model with information spreading of awareness, Chaos. Soliton. Fract., 119 (2019), 118-–125. https://doi.org/10.1016/j.chaos.2018.12.017 doi: 10.1016/j.chaos.2018.12.017
[17]	A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, et al., Early dynamics of transmission and control of covid-19: a mathematical modelling study, Lancet. Infect. Dis., 20 (2020), 553–-558. https://doi.org/10.1016/S1473-3099(20)30144-4 doi: 10.1016/S1473-3099(20)30144-4
[18]	K. Prem, Y. Liu, T. W. Russell, A. J. Kucharski, R. M. Eggo, N. Davies, et al., The effect of control strategies to reduce social mixing on outcomes of the covid-19 epidemic in wuhan, china: A modelling study, Lancet. Public. Health, 5 (2020), 261–-270. https://doi.org/10.1016/S2468-2667(20)30073-6 doi: 10.1016/S2468-2667(20)30073-6
[19]	J. L. Sainz-Pardo, J. Valero, Covid-19 and other viruses: Holding back its spreading by massive testing, Expert. Syst. Appl., 186 (2021), 115710. https://doi.org/10.1016/j.eswa.2021.115710 doi: 10.1016/j.eswa.2021.115710
[20]	T. Phan, S. Brozak, B. Pell, A. Gitter, A. Xiao, K. D. Menad, et al., A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data, Sci. Total. Environ., (2022). https://doi.org/10.1016/j.scitotenv.2022.159326
[21]	P. Jithesh, A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on covid-19 dynamics, Comput. Meth. Prog. Biol., 211 (2021), 106402. https://doi.org/10.1016/j.cmpb.2021.106402 doi: 10.1016/j.cmpb.2021.106402
[22]	L. López, X. Rodo, A modified seir model to predict the covid-19 outbreak in spain and italy: Simulating control scenarios and multi-scale epidemics, Results. Phys., 21 (2021), 103746. https://doi.org/10.1016/j.rinp.2020.103746 doi: 10.1016/j.rinp.2020.103746
[23]	T. M. Chen, J. Rui, Q. Wang, A mathematical model for simulating the phase-based transmissibility of a novel coronavirus, Infect. Disease Model., 5 (2020), 248–-258. https://doi.org/10.1186/s40249-020-00640-3 doi: 10.1186/s40249-020-00640-3
[24]	P. Yarsky, Using a genetic algorithm to fit parameters of a covid-19 seir model for us states, Math. Comput. Simulat., 185 (2021), 687–-695. https://doi.org/10.1016/j.matcom.2021.01.022 doi: 10.1016/j.matcom.2021.01.022
[25]	Y. Fang, Y. Nie, M. Penny, Modified seir and ai prediction of the epidemics trend of covid-19 in china under public health interventions, J. Thorac. Dis., 12 (2020), 165. https://doi.org/10.21037/jtd.2020.02.64 doi: 10.21037/jtd.2020.02.64
[26]	G. Dudek, P. Pelka, S. Smyl, A hybrid residual dilated lstm and exponential smoothing model for midterm electric load forecasting, IEEE. T. Neur. Net. Lear., 33 (2021), 2879–-2891. https://doi.org/10.1109/TNNLS.2020.3046629 doi: 10.1109/TNNLS.2020.3046629
[27]	S. Smyl, A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting, Int. J. Forecast., 36 (2020), 75–-85. https://doi.org/10.1016/j.ijforecast.2019.03.017 doi: 10.1016/j.ijforecast.2019.03.017
[28]	Y. Polyvianna, D. Chumachenko, T. Chumachenko, Computer aided system of time series analysis methods for forecasting the epidemics outbreaks, EDAC (2019). https://doi.org/10.1109/CADSM.2019.8779344 doi: 10.1109/CADSM.2019.8779344
[29]	B. Seong, K. Lee, Intervention analysis based on exponential smoothing methods: Applications to 9/11 and covid-19 effects, Econ. Model, (2020). https://doi.org/10.1016/j.econmod.2020.11.014 doi: 10.1016/j.econmod.2020.11.014
[30]	H. Li, R. Zheng, Q. Zheng, W. Jiang, X. Zhang, W. Wang, et al., Predicting the number of visceral leishmaniasis cases in Kashgar, Xinjiang, China using the ARIMA-EGARCH model, Asian. Pac. J. Trop. Med., 13 (2020), 81–89. https://doi.org/10.4103/1995-7645.275416 doi: 10.4103/1995-7645.275416
[31]	M. K. Lee, J. H. Paik, I. S. Na, Outbreak prediction of hepatitis a in korea based on statistical analysis and lstm network, ICAIIC (2020). https://doi.org/10.1109/ICAIIC48513.2020.9065082 doi: 10.1109/ICAIIC48513.2020.9065082
[32]	S. A. Salama, M. Lavie, M. D. Buck, J. V. Damme, S. Struyf, Cytokines and serum amyloid A in the pathogenesis of hepatitis C virus infection, Cytokine. Growth. F R, 50 (2019), 29–42. https://doi.org/10.1016/j.cytogfr.2019.10.006 doi: 10.1016/j.cytogfr.2019.10.006
[33]	C. Yu, C. Xu, Y. Li, S. Yao, Y. Bai, J. Li, et al., Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model, Infect. Drug. Resist., 14 (2021), 2809–2821. https://doi.org/10.2147/IDR.S304652 doi: 10.2147/IDR.S304652
[34]	R. Ma, X. Zheng, P. Wang, H. Liu, C. Zhang, The prediction and analysis of covid-19 epidemic trend by combining lstm and markov method, Sci. Rep.-UK, 1 (2021). https://doi.org/10.1038/s41598-021-97037-5 doi: 10.1038/s41598-021-97037-5
[35]	A. Sherstinsky, Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network, Phys. D, 404 (2020), 132306. https://doi.org/10.1016/j.physd.2019.132306 doi: 10.1016/j.physd.2019.132306
[36]	Q. Wang, C. Feng, Y. Xu, H. Zhong, V. S. Sheng, A novel privacy-preserving speech recognition framework using bidirectional lstm, Int. J. Cloud. Appl. Com., 9 (2020), 1–13. https://doi.org/10.1186/s13677-020-00186-7 doi: 10.1186/s13677-020-00186-7
[37]	Z. Karevan, J. A. Suykens, Transductive lstm for time-series prediction: An application to weather forecasting, Neural Networks, 125 (2020), 1–-9. https://doi.org/10.1016/j.neunet.2019.12.030 doi: 10.1016/j.neunet.2019.12.030
[38]	T. Y. Kim, S. B. Cho, Predicting residential energy consumption using CNN-LSTM neural networks, Energy, 182 (2019), 72–81. https://doi.org/10.1016/j.energy.2019.05.230 doi: 10.1016/j.energy.2019.05.230
[39]	X. Yan, W. Weihan, M. Chang, Research on financial assets transaction prediction model based on LSTM neural network, Neural. Comput. Appl., 33 (2021), 257–270. https://doi.org/10.1007/s00521-020-04992-7 doi: 10.1007/s00521-020-04992-7
[40]	J. Qian, X. Qiu, X. Tan, Q. Li, J. Chen, X. Jiang, An attentive LSTM-based approach for adverse drug reactions prediction, Appl. Intell., (2022), 1–15. https://doi.org/10.1007/s10489-022-03721-y doi: 10.1007/s10489-022-03721-y
[41]	S. Dutta, S. K. Bandyopadhyay, Machine learning approach for confirmation of COVID-19 cases: Positive, negative, death and release, MedRxiv, (2020). https://doi.org/10.1101/2020.03.25.20043505 doi: 10.1101/2020.03.25.20043505

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)