Prediction of ILI following the COVID-19 pandemic in China by using a partial differential equation

doi:10.1088/1674-1056/ad6f90

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

[1]	Exploring reservoir computing: Implementation via double stochastic nanowire networks Jian-Feng Tang(唐健峰), Lei Xia(夏磊), Guang-Li Li(李广隶), Jun Fu(付军), Shukai Duan(段书凯), and Lidan Wang(王丽丹). Chin. Phys. B, 2024, 33(3): 037302.
[2]	A novel MgHe compound under high pressure Jurong Zhang(张车荣), Lebin Chang(常乐斌), Suchen Ji(纪苏宸), Lancing Guo(郭兰慈), and Yuhao Fu(付钰豪). Chin. Phys. B, 2024, 33(11): 116202.
[3]	Crysformer: An attention-based graph neural network for properties prediction of crystals Tian Wang(王田), Jiahui Chen(陈家辉), Jing Teng(滕婧), Jingang Shi(史金钢),Xinhua Zeng(曾新华), and Hichem Snoussi. Chin. Phys. B, 2023, 32(9): 090703.
[4]	Prediction of multifaceted asymmetric radiation from the edge movement in density-limit disruptive plasmas on Experimental Advanced Superconducting Tokamak using random forest Wenhui Hu(胡文慧), Jilei Hou(侯吉磊), Zhengping Luo(罗正平), Yao Huang(黄耀), Dalong Chen(陈大龙),Bingjia Xiao(肖炳甲), Qiping Yuan(袁旗平), Yanmin Duan(段艳敏), Jiansheng Hu(胡建生),Guizhong Zuo(左桂忠), and Jiangang Li(李建刚). Chin. Phys. B, 2023, 32(7): 075211.
[5]	New MgO-H₂O compounds at extreme conditions Lanci Guo(郭兰慈) and Jurong Zhang(张车荣). Chin. Phys. B, 2023, 32(7): 076201.
[6]	Disruption prediction based on fusion feature extractor on J-TEXT Wei Zheng(郑玮), Fengming Xue(薛凤鸣), Zhongyong Chen(陈忠勇), Chengshuo Shen(沈呈硕), Xinkun Ai(艾鑫坤), Yu Zhong(钟昱), Nengchao Wang(王能超), Ming Zhang(张明),Yonghua Ding(丁永华), Zhipeng Chen(陈志鹏), Zhoujun Yang(杨州军), and Yuan Pan(潘垣). Chin. Phys. B, 2023, 32(7): 075203.
[7]	Model considering panic emotion and personality traits for crowd evacuation Hua-Kai Sun(孙华锴) and Chang-Kun Chen(陈长坤). Chin. Phys. B, 2023, 32(5): 050401.
[8]	Inatorial forecasting method considering macro and micro characteristics of chaotic traffic flow Yue Hou(侯越), Di Zhang(张迪), Da Li(李达), and Ping Yang(杨萍). Chin. Phys. B, 2023, 32(10): 100508.
[9]	Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network Hai-Yang Meng(孟海洋), Zi-Xiang Xu(徐自翔), Jing Yang(杨京), Bin Liang(梁彬), and Jian-Chun Cheng(程建春). Chin. Phys. B, 2022, 31(6): 064305.
[10]	Loss prediction of three-level amplified spontaneous emission sources in radiation environment Shen Tan(谭深), Yan Li(李彦), Hao-Shi Zhang(张浩石), Xiao-Wei Wang(王晓伟), and Jing Jin(金靖). Chin. Phys. B, 2022, 31(6): 064211.
[11]	Traffic flow prediction based on BILSTM model and data denoising scheme Zhong-Yu Li(李中昱), Hong-Xia Ge(葛红霞), and Rong-Jun Cheng(程荣军). Chin. Phys. B, 2022, 31(4): 040502.
[12]	Boron at tera-Pascal pressures Peiju Hu(胡佩菊), Junhao Peng(彭俊豪), Xing Xie(谢兴), Minru Wen(文敏儒),Xin Zhang(张欣), Fugen Wu(吴福根), and Huafeng Dong(董华锋). Chin. Phys. B, 2022, 31(3): 036301.
[13]	RNAGCN: RNA tertiary structure assessment with a graph convolutional network Chengwei Deng(邓成伟), Yunxin Tang(唐蕴芯), Jian Zhang(张建), Wenfei Li(李文飞), Jun Wang(王骏), and Wei Wang(王炜). Chin. Phys. B, 2022, 31(11): 118702.
[14]	Pressure-induced phase transition in transition metal trifluorides Peng Liu(刘鹏), Meiling Xu(徐美玲), Jian Lv(吕健), Pengyue Gao(高朋越), Chengxi Huang(黄呈熙), Yinwei Li(李印威), Jianyun Wang(王建云), Yanchao Wang(王彦超), and Mi Zhou(周密). Chin. Phys. B, 2022, 31(10): 106104.
[15]	Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China Ru-Qi Li(李汝琦), Yu-Rong Song(宋玉蓉), and Guo-Ping Jiang(蒋国平). Chin. Phys. B, 2021, 30(12): 120202.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Prediction of ILI following the COVID-19 pandemic in China by using a partial differential equation

Cite this article:

Online attention