Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China

doi:10.1088/1674-1056/ac2b16

中国物理B ›› 2021, Vol. 30 ›› Issue (12): 120202-120202.doi: 10.1088/1674-1056/ac2b16

所属专题： SPECIAL TOPIC — Interdisciplinary physics: Complex network dynamics and emerging technologies

Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China

Ru-Qi Li(李汝琦)¹, Yu-Rong Song(宋玉蓉)², and Guo-Ping Jiang(蒋国平)^2,†

1 School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2 College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

收稿日期:2021-07-17 修回日期:2021-09-13 接受日期:2021-09-29 出版日期:2021-11-15 发布日期:2021-11-25
通讯作者: Guo-Ping Jiang E-mail:jianggp@njupt.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 61672298, 61873326, and 61802155) and the Philosophy Social Science Research Key Project Fund of Jiangsu University (Grant No. 2018SJZDI142).

Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China

Ru-Qi Li(李汝琦)¹, Yu-Rong Song(宋玉蓉)², and Guo-Ping Jiang(蒋国平)^2,†

1 School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2 College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Received:2021-07-17 Revised:2021-09-13 Accepted:2021-09-29 Online:2021-11-15 Published:2021-11-25
Contact: Guo-Ping Jiang E-mail:jianggp@njupt.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 61672298, 61873326, and 61802155) and the Philosophy Social Science Research Key Project Fund of Jiangsu University (Grant No. 2018SJZDI142).

摘要/Abstract

摘要： Since December 2019, the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade, national policies and the natural environment. To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy, we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations (PDEs), which captures epidemic diffusion along the edges of a network driven by population flow data. In this paper, we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19. Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models. Furthermore, we study the effectiveness of intervention measures, such as traffic lockdowns and social distancing, which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model. To our knowledge, this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.

关键词: partial differential equations, intervention measures, Baidu Migration Data, COVID-19 prediction

Abstract: Since December 2019, the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade, national policies and the natural environment. To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy, we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations (PDEs), which captures epidemic diffusion along the edges of a network driven by population flow data. In this paper, we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19. Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models. Furthermore, we study the effectiveness of intervention measures, such as traffic lockdowns and social distancing, which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model. To our knowledge, this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.

Key words: partial differential equations, intervention measures, Baidu Migration Data, COVID-19 prediction

中图分类号: (Partial differential equations)

02.30.Jr

88.10.gc (Simulation; prediction models) 02.60.Ed (Interpolation; curve fitting) 05.10.-a (Computational methods in statistical physics and nonlinear dynamics)

Ru-Qi Li(李汝琦), Yu-Rong Song(宋玉蓉), and Guo-Ping Jiang(蒋国平). Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China[J]. 中国物理B, 2021, 30(12): 120202-120202.

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Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China

Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China

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