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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.
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Keywords:
- COVID-19 /
- dynamical model /
- prediction for epidemic spreading
[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
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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
Figure 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
Figure 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 $ )Figure 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 $ )Figure 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
Figure 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
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[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 69 080701
Google Scholar
Wang C, Yan J, Wang X, Li M 2020 Acta Phys. Sin. 69 080701
Google Scholar
[5] 曹文静, 刘小菲, 韩卓, 冯鑫, 张琳, 刘肖凡, 许小可, 吴晔 2020 69 090203
Google 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 090203
Google Scholar
[6] 戴碧涛, 谭索怡, 陈洒然, 蔡梦思, 秦烁, 吕欣 2021 70 068903
Google Scholar
Dai B T, Tan S Y, Chen S R, Cai M S, Qin S, Lu X 2021 Acta Phys. Sin. 70 068903
Google 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 e0268023
Google Scholar
[8] Ghosal S, Sengupta S, Majumder M, Sinha B 2020 Diabetes Metab. Syndr. 14 311
Google Scholar
[9] Hoertel B, Blachier M, Blanco C, Olfson M, Massetti M, Rico M S, Limosin F, Leleu H 2020 Nat. Med. 26 1417
Google 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 e0236618
Google Scholar
[11] Hernandez-Matamoros A, Fujita H, Hayashi T, Perez-Meana H 2020 APPL Soft Comput. 96 106610
Google 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 109942
Google Scholar
[14] Zhao Y F, Shou M H, Wang Z X 2020 Int. J. Environ. Res. Public Health 17 4582
Google Scholar
[15] Siwiak M, Szczesny P, Siwiak M 2020 PeerJ 8 e9548
Google 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. 12 165
Google Scholar
[18] Chatterjee K, Chatterjee K, Kumar A, Shankar S 2020 Med. J. Armed Forces India 76 147
Google Scholar
[19] Kissler S M, Tedijanto C, Goldstein E, Grad Y H, Lipsitch M 2020 Science 368 860
Google 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 69 240201
Google Scholar
Sun H C, Liu X F, Xu X K, Wu Y 2020 Acta Phys. Sin. 69 240201
Google 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. 2 283
Google 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 138762
Google Scholar
[25] Chimmula V K R, Zhang L 2020 Chaos, Solitons Fractals 135 109864
Google Scholar
[26] Ardabili S, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy A, Reuter U, Rabczuk T, Atkinson P 2020 Algorithms 13 249
Google Scholar
[27] Sujath R, Chatterjee J M, Hassanien A E 2020 Stoch. Environ. Res. Risk. Assess 34 959
Google Scholar
[28] Arora P, Kumar H, Panigrahi B K 2020 Chaos, Solitons Fractals 139 110017
Google Scholar
[29] Fernandez A, Obiechina N, Koh J, Hong A, Nandi A, Reynolds T M 2021 Int. J. Clin. Pract. 75 e13974
Google Scholar
[30] Muhammad L J, Islam M M, Usman S S, Ayon S S 2020 SN Comput. Sci. 1 206
Google Scholar
[31] https://baijiahao.baidu.com/s?id=1752161828062869006&wfr=spider&for=pc [2022-12-20]
[32] [33]
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