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Chin. Phys. B, 2024, Vol. 33(11): 110201    DOI: 10.1088/1674-1056/ad6f90
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Prediction of ILI following the COVID-19 pandemic in China by using a partial differential equation

Xu Zhang(张栩)1, Yu-Rong Song(宋玉蓉)2,†, and Ru-Qi Li(李汝琦)1
1 School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
2 College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Abstract  The COVID-19 outbreak has significantly disrupted the lives of individuals worldwide. Following the lifting of COVID-19 interventions, there is a heightened risk of future outbreaks from other circulating respiratory infections, such as influenza-like illness (ILI). Accurate prediction models for ILI cases are crucial in enabling governments to implement necessary measures and persuade individuals to adopt personal precautions against the disease. This paper aims to provide a forecasting model for ILI cases with actual cases. We propose a specific model utilizing the partial differential equation (PDE) that will be developed and validated using real-world data obtained from the Chinese National Influenza Center. Our model combines the effects of transboundary spread among regions in China mainland and human activities' impact on ILI transmission dynamics. The simulated results demonstrate that our model achieves excellent predictive performance. Additionally, relevant factors influencing the dissemination are further examined in our analysis. Furthermore, we investigate the effectiveness of travel restrictions on ILI cases. Results can be used to utilize to mitigate the spread of disease.
Keywords:  partial differential equations      influenza      SIS model      prediction  
Received:  15 May 2024      Revised:  12 August 2024      Accepted manuscript online:  15 August 2024
PACS:  02.30.Jr (Partial differential equations)  
  88.10.gc (Simulation; prediction models)  
  02.60.Cb (Numerical simulation; solution of equations)  
  87.23.Kg (Dynamics of evolution)  
Fund: This research has been supported by the National Natural Science Foundation of China (Grant No. 62373197) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX18_0892).
Corresponding Authors:  Yu-Rong Song     E-mail:  songyr@njupt.edu.cn

Cite this article: 

Xu Zhang(张栩), Yu-Rong Song(宋玉蓉), and Ru-Qi Li(李汝琦) Prediction of ILI following the COVID-19 pandemic in China by using a partial differential equation 2024 Chin. Phys. B 33 110201

[1] Bosetti P, Tran Kiem C, Andronico A, Colizza V, Yazdanpanah Y, Fontanet A, Benamouzig D and Cauchemez S 2022 BMC Med. 20 33
[2] Zhang Y, Tao Y, Shyu M, Perry L, Warde P, Messinger D and Song C 2022 Sci. Rep. 12 3044
[3] Huang Y, Shi H, Forgacs D and Ross T 2024 Vaccine 42 1184
[4] Zhu Y, Shen R, Dong H and Wang W 2024 Chin. Phys. B 33 058301
[5] Davies N, Kucharski A, Eggo R, Gimma A and Liu Y 2020 Lancet Public Health 5 e375
[6] Li Y, Campbell H, Kulkarni D, Harpur A, Nundy M, Wang X and Nair H 2021 Lancet Infect. Dis. 21 193
[7] Kim J, Roh Y, Ahn J, Kim M, Huh K, Jung J and Kang J 2021 Int. J. Infect. Dis. 110 29
[8] Huang Q, Wood T, Jelley L, Jennings T, Jefferies S, Daniells K, Nesdale A, Dowell T, Turner N, Campbell-Stokes P, et al. 2021 Nat. Commun. 12 1001
[9] Feng L, Zhang T, Wang Q, Xie Y, Peng Z, Zheng J, Qin Y, Zhang M, Lai S, Wang D, Feng Z, Li Z and Gao G 2021 Nat. Commun. 12 3249
[10] Yang X, Jiang H, Kang Y, et al. 2022 Chin. Phys. B 31 078901
[11] Wang X and Wang L 2022 Chin. Phys. B 31 080204
[12] Li R, Song Y and Jiang G 2021 Chin. Phys. B 30 120202
[13] Liang J, Lin Z, Wang Y, et al. 2024 Front. Cell. Infect. Microbiol. 14 1347710
[14] Zhang N, Jia W, Lei H, Wang P, Zhao P Y, Guo Y, et al. 2020 Clin. Infect. Dis. 73 e1142
[15] Flaxman S, Mishra S, Gandy A, Unwin H, Mellan T, Coupland H, Whittaker C, Zhu H, Berah T and Eaton J 2020 Nature 584 257
[16] Lai S, Ruktanonchai N, Zhou L, Prosper O, Luo W, Floyd J, Wesolowski A, Santillana M, Zhang C, Du X, Yu H and Tatem A 2020 Nature 585 410
[17] Cowling B, Ali S, Ng T, et al. 2020 Lancet Public Health 5 e279
[18] Xiao J, Dai J, Hu J, et al. 2021 Lancet Reg. Health West Pac. 17 100282
[19] Ursinus J, Vrijmoeth H, Harms M, Tulen A, Knoop H, Gauw S, Zomer T, et al. 2021 Lancet Reg. Health Eur. 6 100142
[20] Cheng W, Zhou H, Ye Y, Chen Y, Jing F, Cao Z, Zeng D and Zhang Q 2023 Chaos 33 013124
[21] Chao S, Wang Y, Wu B, et al. 2024 Front. Cell. Infect. Microbiol. 13 1351814
[22] Oguz M and Senel S 2024 Human Vaccines & Immunotherapeutics 20 2350090
[23] Chen H and Xiao M 2024 BMC Infect. Dis. 24 432
[24] Nie Y, Su S, Lin T, et al. 2023 Commun. Nonlinear Sci. Numer. Simulat. 127 107594
[25] Nie Y, Zhong X, Wu T, et al. 2022 Journal of King Saud University -Computer and Information Sciences 34 2871
[26] Wang W, Nie Y, Li W, et al. 2024 Phys. Rep. 1056 1
[27] Riley S, Eames K, Isham V, Mollison D and Trapman P 2015 Epidemics 10 68
[28] Murray J D 2002 Photosynthetica 40 414
[29] Wang Y, Wang J and Zhang L 2010 Appl. Math. Comput. 217 1965
[30] Sun G 2012 Nonlinear Dyn. 69 1097
[31] Li J and Nie L 2024 Qual. Theory Dyn. Syst. 23 198
[32] Wang Y, Xu K, Kang Y, Wang H, Wang F and Avram A 2020 Int. J. Environ. Res. Public Health 17 678
[33] Wang H and Yamamoto N 2020 Math. Biosci. Eng. 17 4891
[34] Pellis L, Ball F, Bansal S, Eames K, House T, Isham V and Trapman P 2015 Epidemics 10 58
[35] Balcan D, Colizza V, Goncalves B, et al. 2009 Proc. Natl. Acade. Sci. USA 106 21484
[36] Lions P and Toscani G 1997 Revista Matemática Iberoamericana 13 473
[37] Oseledets I V 2011 SIAM J. Sci. Comput. 33 2295
[38] Lagarias J C, Reeds J A, Wright M H and Wright P E 1998 SIAM J. Optim. 9 112
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