搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

考虑出行行为调整的疫情传播模型与新冠疫情预测

张晶 王海英 顾长贵 杨会杰

引用本文:
Citation:

考虑出行行为调整的疫情传播模型与新冠疫情预测

张晶, 王海英, 顾长贵, 杨会杰

Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Zhang Jing, Wang Hai-Ying, Gu Chang-Gui, Yang Hui-Jie
PDF
HTML
导出引用
  • 由于新冠病毒不断变异, 在很长的时期内疫情会多次爆发, 每次有不同的特点. 对局部地区爆发的每一波疫情进行预测, 成为人们制定应对策略的关键. 在宏观层面对疫情防控措施优化, 意味着疫情演化数据的缺乏, 这给基于实证数据的疫情预测带来了特殊的困难. 考虑疫情与出行行为的相互影响, 本文提出了一个改进的虫口模型, 用以描述新冠疫情传播动力学过程, 试图利用少量疫情相关数据对局部地区爆发的某一特定疫情进行预测. 实证分析表明, 该模型可以很好地复现上海市2022年3月1日到6月28日发布的新冠病毒阳性感染者数据. 采用这一模型对上海市2022年12月以来的疫情趋势和关键节点进行了预测. 建议决策部门按照统计学抽样原则, 建立和完善疫情监测系统, 为疫情预测提供可靠的数据.
    Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people’s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took “ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word “ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word “ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers.
      通信作者: 杨会杰, hjyang@usst.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 1227051117, 11875042, 11505114)资助的课题
      Corresponding author: Yang Hui-Jie, hjyang@usst.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 1227051117, 11875042, 11505114)
    [1]

    中华人民共和国中央人民政府国务防联防联控机制综合组 http://www.gov.cn/xinwen/2022-11/11/content_5726122.htm [2022-11-11]

    The State Council’s Joint Prevention and Control Mechanism against the COVID-19 Epidemic of the Central People’s Government of the People’s Republic of China

    [2]

    中华人民共和国中央人民政府国务防联防联控机制综合组 http://www.gov.cn/xinwen/2022-12/07/content_5730443.htm [2022-12-07]

    The State Council’s Joint Prevention and Control Mechanism against the COVID-19 Epidemic of the Central People’s Government of the People’s Republic of China

    [3]

    Leung K, Leung G M, Wu J T 2022 MedRxiv: 2022.12.14.22283460

    [4]

    王聪, 严洁, 王旭, 李敏 2020 物理学报 69 080701Google Scholar

    Wang C, Yan J, Wang X, Li M 2020 Acta Phys. Sin. 69 080701Google Scholar

    [5]

    曹文静, 刘小菲, 韩卓, 冯鑫, 张琳, 刘肖凡, 许小可, 吴晔 2020 物理学报 69 090203Google Scholar

    Cao W J, Liu X F, Han Z, Feng X, Zhang L, Liu X F, Xu X K, Wu Y 2020 Acta Phys. Sin. 69 090203Google Scholar

    [6]

    戴碧涛, 谭索怡, 陈洒然, 蔡梦思, 秦烁, 吕欣 2021 物理学报 70 068903Google Scholar

    Dai B T, Tan S Y, Chen S R, Cai M S, Qin S, Lu X 2021 Acta Phys. Sin. 70 068903Google Scholar

    [7]

    Necesito I V, Velasco J M S, Jung J, Bae Y H, Lee J H, Kim S J, Kim H S 2022 PLoS One 17 e0268023Google Scholar

    [8]

    Ghosal S, Sengupta S, Majumder M, Sinha B 2020 Diabetes Metab. Syndr. 14 311Google Scholar

    [9]

    Hoertel B, Blachier M, Blanco C, Olfson M, Massetti M, Rico M S, Limosin F, Leleu H 2020 Nat. Med. 26 1417Google Scholar

    [10]

    Zhao Z R, Chen A, Hou W, Graham J M, Li H F, Richman P S, Thode H C, Singer A J, Duong T Q 2020 PLoS One 15 e0236618Google Scholar

    [11]

    Hernandez-Matamoros A, Fujita H, Hayashi T, Perez-Meana H 2020 APPL Soft Comput. 96 106610Google Scholar

    [12]

    Alazab M, Awajan A, Mesleh A, Abraham A, Jatana V, Alhyari S 2020 INT J. Comput. Inf. Syst. Ind. Manag. Appl. 12 168

    [13]

    Parbat D, Chakraborty M 2020 Chaos, Solitons Fractals 138 109942Google Scholar

    [14]

    Zhao Y F, Shou M H, Wang Z X 2020 Int. J. Environ. Res. Public Health 17 4582Google Scholar

    [15]

    Siwiak M, Szczesny P, Siwiak M 2020 PeerJ 8 e9548Google Scholar

    [16]

    Bhandari S, Tak A, Gupta J, Patel B, Shukla J, Shaktawat A S, Singhal S, Saini A, Kakkar S, Dube A, Dia S, Dia M, Wehner T C 2020 Research Square https://doi.org/10.21203/rs.3.rs-40385/v1

    [17]

    Yang Z F, Zeng Z Q, Wang K, Wong S S, Liang W H, Zanin M, Liu P, Cao X D, Gao Z Q, Mai Z T, Liang J Y, Liu X Q, Li S Y, Li Y M, Ye F, Guan W J, Yang Y F, Li F, Luo S M, Xie Y Q, Liu B, Wang Z L, Zhang S B, Wang Y N, Zhong N S, He J X 2020 J. Throac. Dis. 12 165Google Scholar

    [18]

    Chatterjee K, Chatterjee K, Kumar A, Shankar S 2020 Med. J. Armed Forces India 76 147Google Scholar

    [19]

    Kissler S M, Tedijanto C, Goldstein E, Grad Y H, Lipsitch M 2020 Science 368 860Google Scholar

    [20]

    Zhao Y J, Huang J P, Zhang L, Lian X B, Wang D F 2022 The Innovation 3 100240

    [21]

    孙皓宸, 刘肖凡, 许小可, 吴晔 2020 物理学报 69 240201Google Scholar

    Sun H C, Liu X F, Xu X K, Wu Y 2020 Acta Phys. Sin. 69 240201Google Scholar

    [22]

    Yan L, Zhang H T, Goncalves J, Xiao Y, Wang M L, Guo Y Q, Sun C, Tang X C, Jing L, Zhang M Y, Huang X, Xiao Y, Cao H S, Chen Y Y, Ren T X, Wang F, Xiao Y R, Huang S F, Tan X, Huang N N, Jiao B, Cheng C, Zhang Y, Luo A L, Mombaerts L, Jin J Y, Cao Z G, Li S S, Xu H, Yuan Y 2020 Nat. Mach. Intell. 2 283Google Scholar

    [23]

    Hu Z X, Ge Q Y, Li S D, Jin L, Xiong M M 2020 arXiv: 2002.07112 v2[q-bio.OT]

    [24]

    Tomar A, Gupta N 2020 Sci. Total Environ. 728 138762Google Scholar

    [25]

    Chimmula V K R, Zhang L 2020 Chaos, Solitons Fractals 135 109864Google Scholar

    [26]

    Ardabili S, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy A, Reuter U, Rabczuk T, Atkinson P 2020 Algorithms 13 249Google Scholar

    [27]

    Sujath R, Chatterjee J M, Hassanien A E 2020 Stoch. Environ. Res. Risk. Assess 34 959Google Scholar

    [28]

    Arora P, Kumar H, Panigrahi B K 2020 Chaos, Solitons Fractals 139 110017Google Scholar

    [29]

    Fernandez A, Obiechina N, Koh J, Hong A, Nandi A, Reynolds T M 2021 Int. J. Clin. Pract. 75 e13974Google Scholar

    [30]

    Muhammad L J, Islam M M, Usman S S, Ayon S S 2020 SN Comput. Sci. 1 206Google Scholar

    [31]

    https://baijiahao.baidu.com/s?id=1752161828062869006&wfr=spider&for=pc [2022-12-20]

    [32]

    https://index.baidu.com/v2/index.html [2022-12-20]

    [33]

    https://www.shanghai.gov.cn/nw9819/index.html [2022-12-26]

  • 图 1  复现中国上海市2022年3月1日至6月28日新冠病毒感染阳性病例数

    Fig. 1.  Reproduction of the infected numbers for COVID-19 in the duration from March 1 to June 28, 2022 in Shanghai, China

    图 2  预测中国上海市2022年12月1日开始爆发的新冠疫病毒感染阳性病例数

    Fig. 2.  Prediction of the infected numbers for COVID-19 starting from December 1 in Shanghai, China

    图 3  出行行为参数对疫情演化的影响($ \gamma = 0.5 $)

    Fig. 3.  Impact of travel behavior on epidemic process ($ \gamma = 0.5 $)

    图 4  出行行为参数对疫情演化的影响($ \gamma = 0.8 $)

    Fig. 4.  Impact of travel behavior on epidemic process ($ \gamma = 0.8 $)

    图 5  上海阳性感染者预测曲线与地铁乘客数量曲线比较 (a)上海2022年12月到2023年2月新冠阳性每日新增预测; (b)地铁乘客人数, 曲线向右移动了8天

    Fig. 5.  Comparison of the prediction of infected number per day with the number of metro passengers in Shanghai China: (a) Prediction of COVID-19 virus infected numbers in the duration from Dec. 2022 to Feb. 2023 in Shanghai, China; (b) metro passenger numbers, where the time has been shifted rightly 8 days

  • [1]

    中华人民共和国中央人民政府国务防联防联控机制综合组 http://www.gov.cn/xinwen/2022-11/11/content_5726122.htm [2022-11-11]

    The State Council’s Joint Prevention and Control Mechanism against the COVID-19 Epidemic of the Central People’s Government of the People’s Republic of China

    [2]

    中华人民共和国中央人民政府国务防联防联控机制综合组 http://www.gov.cn/xinwen/2022-12/07/content_5730443.htm [2022-12-07]

    The State Council’s Joint Prevention and Control Mechanism against the COVID-19 Epidemic of the Central People’s Government of the People’s Republic of China

    [3]

    Leung K, Leung G M, Wu J T 2022 MedRxiv: 2022.12.14.22283460

    [4]

    王聪, 严洁, 王旭, 李敏 2020 物理学报 69 080701Google Scholar

    Wang C, Yan J, Wang X, Li M 2020 Acta Phys. Sin. 69 080701Google Scholar

    [5]

    曹文静, 刘小菲, 韩卓, 冯鑫, 张琳, 刘肖凡, 许小可, 吴晔 2020 物理学报 69 090203Google Scholar

    Cao W J, Liu X F, Han Z, Feng X, Zhang L, Liu X F, Xu X K, Wu Y 2020 Acta Phys. Sin. 69 090203Google Scholar

    [6]

    戴碧涛, 谭索怡, 陈洒然, 蔡梦思, 秦烁, 吕欣 2021 物理学报 70 068903Google Scholar

    Dai B T, Tan S Y, Chen S R, Cai M S, Qin S, Lu X 2021 Acta Phys. Sin. 70 068903Google Scholar

    [7]

    Necesito I V, Velasco J M S, Jung J, Bae Y H, Lee J H, Kim S J, Kim H S 2022 PLoS One 17 e0268023Google Scholar

    [8]

    Ghosal S, Sengupta S, Majumder M, Sinha B 2020 Diabetes Metab. Syndr. 14 311Google Scholar

    [9]

    Hoertel B, Blachier M, Blanco C, Olfson M, Massetti M, Rico M S, Limosin F, Leleu H 2020 Nat. Med. 26 1417Google Scholar

    [10]

    Zhao Z R, Chen A, Hou W, Graham J M, Li H F, Richman P S, Thode H C, Singer A J, Duong T Q 2020 PLoS One 15 e0236618Google Scholar

    [11]

    Hernandez-Matamoros A, Fujita H, Hayashi T, Perez-Meana H 2020 APPL Soft Comput. 96 106610Google Scholar

    [12]

    Alazab M, Awajan A, Mesleh A, Abraham A, Jatana V, Alhyari S 2020 INT J. Comput. Inf. Syst. Ind. Manag. Appl. 12 168

    [13]

    Parbat D, Chakraborty M 2020 Chaos, Solitons Fractals 138 109942Google Scholar

    [14]

    Zhao Y F, Shou M H, Wang Z X 2020 Int. J. Environ. Res. Public Health 17 4582Google Scholar

    [15]

    Siwiak M, Szczesny P, Siwiak M 2020 PeerJ 8 e9548Google Scholar

    [16]

    Bhandari S, Tak A, Gupta J, Patel B, Shukla J, Shaktawat A S, Singhal S, Saini A, Kakkar S, Dube A, Dia S, Dia M, Wehner T C 2020 Research Square https://doi.org/10.21203/rs.3.rs-40385/v1

    [17]

    Yang Z F, Zeng Z Q, Wang K, Wong S S, Liang W H, Zanin M, Liu P, Cao X D, Gao Z Q, Mai Z T, Liang J Y, Liu X Q, Li S Y, Li Y M, Ye F, Guan W J, Yang Y F, Li F, Luo S M, Xie Y Q, Liu B, Wang Z L, Zhang S B, Wang Y N, Zhong N S, He J X 2020 J. Throac. Dis. 12 165Google Scholar

    [18]

    Chatterjee K, Chatterjee K, Kumar A, Shankar S 2020 Med. J. Armed Forces India 76 147Google Scholar

    [19]

    Kissler S M, Tedijanto C, Goldstein E, Grad Y H, Lipsitch M 2020 Science 368 860Google Scholar

    [20]

    Zhao Y J, Huang J P, Zhang L, Lian X B, Wang D F 2022 The Innovation 3 100240

    [21]

    孙皓宸, 刘肖凡, 许小可, 吴晔 2020 物理学报 69 240201Google Scholar

    Sun H C, Liu X F, Xu X K, Wu Y 2020 Acta Phys. Sin. 69 240201Google Scholar

    [22]

    Yan L, Zhang H T, Goncalves J, Xiao Y, Wang M L, Guo Y Q, Sun C, Tang X C, Jing L, Zhang M Y, Huang X, Xiao Y, Cao H S, Chen Y Y, Ren T X, Wang F, Xiao Y R, Huang S F, Tan X, Huang N N, Jiao B, Cheng C, Zhang Y, Luo A L, Mombaerts L, Jin J Y, Cao Z G, Li S S, Xu H, Yuan Y 2020 Nat. Mach. Intell. 2 283Google Scholar

    [23]

    Hu Z X, Ge Q Y, Li S D, Jin L, Xiong M M 2020 arXiv: 2002.07112 v2[q-bio.OT]

    [24]

    Tomar A, Gupta N 2020 Sci. Total Environ. 728 138762Google Scholar

    [25]

    Chimmula V K R, Zhang L 2020 Chaos, Solitons Fractals 135 109864Google Scholar

    [26]

    Ardabili S, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy A, Reuter U, Rabczuk T, Atkinson P 2020 Algorithms 13 249Google Scholar

    [27]

    Sujath R, Chatterjee J M, Hassanien A E 2020 Stoch. Environ. Res. Risk. Assess 34 959Google Scholar

    [28]

    Arora P, Kumar H, Panigrahi B K 2020 Chaos, Solitons Fractals 139 110017Google Scholar

    [29]

    Fernandez A, Obiechina N, Koh J, Hong A, Nandi A, Reynolds T M 2021 Int. J. Clin. Pract. 75 e13974Google Scholar

    [30]

    Muhammad L J, Islam M M, Usman S S, Ayon S S 2020 SN Comput. Sci. 1 206Google Scholar

    [31]

    https://baijiahao.baidu.com/s?id=1752161828062869006&wfr=spider&for=pc [2022-12-20]

    [32]

    https://index.baidu.com/v2/index.html [2022-12-20]

    [33]

    https://www.shanghai.gov.cn/nw9819/index.html [2022-12-26]

  • [1] 闫洪波, 黄海涛, 汪建新, 黄健, 谢凯. 超磁致伸缩材料在不同外部条件下的磁滞模型预测. 物理学报, 2024, 73(22): 228501. doi: 10.7498/aps.73.20241219
    [2] 宗志成, 潘东楷, 邓世琛, 万骁, 杨哩娜, 马登科, 杨诺. 混合失配模型预测金属/半导体界面热导. 物理学报, 2023, 72(3): 034401. doi: 10.7498/aps.72.20221981
    [3] 罗方芳, 蔡志涛, 黄艳东. 蛋白质pKa预测模型研究进展. 物理学报, 2023, 72(24): 248704. doi: 10.7498/aps.72.20231356
    [4] 王国强, 张烁, 杨俊元, 许小可. 耦合不同年龄层接触模式的新冠肺炎传播模型. 物理学报, 2021, 70(1): 010201. doi: 10.7498/aps.70.20201371
    [5] 戴碧涛, 谭索怡, 陈洒然, 蔡梦思, 秦烁, 吕欣. 基于手机大数据的中国人口迁徙模式及疫情影响研究. 物理学报, 2021, 70(6): 068903. doi: 10.7498/aps.70.20202084
    [6] 孙皓宸, 刘肖凡, 许小可, 吴晔. 基于连续感染模型的新冠肺炎校园传播与防控策略分析. 物理学报, 2020, 69(24): 240201. doi: 10.7498/aps.69.20201106
    [7] 曹文静, 刘小菲, 韩卓, 冯鑫, 张琳, 刘肖凡, 许小可, 吴晔. 新型冠状病毒肺炎疫情确诊病例的统计分析及自回归建模. 物理学报, 2020, 69(9): 090203. doi: 10.7498/aps.69.20200503
    [8] 李勇军, 尹超, 于会, 刘尊. 基于最大熵模型的微博传播网络中的链路预测. 物理学报, 2016, 65(2): 020501. doi: 10.7498/aps.65.020501
    [9] 张玉梅, 胡小俊, 吴晓军, 白树林, 路纲. 语音信号序列的Volterra预测模型. 物理学报, 2015, 64(20): 200507. doi: 10.7498/aps.64.200507
    [10] 刘宾礼, 唐勇, 罗毅飞, 刘德志, 王瑞田, 汪波. 基于电压变化率的IGBT结温预测模型研究. 物理学报, 2014, 63(17): 177201. doi: 10.7498/aps.63.177201
    [11] 李菁田, 王建录, 张邦强, 荣曦明, 宁西京. 一种预测材料蠕变速率的新模型. 物理学报, 2014, 63(2): 028101. doi: 10.7498/aps.63.028101
    [12] 张彦超, 刘云, 张海峰, 程辉, 熊菲. 基于在线社交网络的信息传播模型. 物理学报, 2011, 60(5): 050501. doi: 10.7498/aps.60.050501
    [13] 杜杰, 曹一家, 刘志坚, 徐立中, 江全元, 郭创新, 陆金桂. 混沌时间序列的局域高阶Volterra滤波器多步预测模型. 物理学报, 2009, 58(9): 5997-6005. doi: 10.7498/aps.58.5997
    [14] 高晓峰, 徐之海, 冯华君, 李奇. 振幅指数衰减正弦干涉信号线性预测模型的求解. 物理学报, 2009, 58(10): 6719-6724. doi: 10.7498/aps.58.6719
    [15] 毛剑琴, 丁海山, 姚健. 基于模糊树模型的混沌时间序列预测. 物理学报, 2009, 58(4): 2220-2230. doi: 10.7498/aps.58.2220
    [16] 李永青, 李希国, 刘紫玉, 罗培燕, 张鹏鸣. Jackiw-Pi模型的新涡旋解. 物理学报, 2007, 56(11): 6178-6182. doi: 10.7498/aps.56.6178
    [17] 郭永存, 曾亿山, 卢德唐. 地层静温预测的非牛顿流体数学模型. 物理学报, 2005, 54(2): 802-806. doi: 10.7498/aps.54.802
    [18] 崔万照, 朱长纯, 保文星, 刘君华. 基于模糊模型支持向量机的混沌时间序列预测. 物理学报, 2005, 54(7): 3009-3018. doi: 10.7498/aps.54.3009
    [19] 王宏伟, 马广富. 基于模糊模型的混沌时间序列预测. 物理学报, 2004, 53(10): 3293-3297. doi: 10.7498/aps.53.3293
    [20] 钱昆明, 林肇华, 戴道生. 自旋波共振激发的新模型. 物理学报, 1983, 32(12): 1547-1556. doi: 10.7498/aps.32.1547
计量
  • 文章访问数:  3797
  • PDF下载量:  96
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-12-25
  • 修回日期:  2023-02-24
  • 上网日期:  2023-04-14
  • 刊出日期:  2023-05-05

/

返回文章
返回