300
Views
12
Downloads
1
Crossref
N/A
WoS
1
Scopus
N/A
CSCD
We used the Bass model to investigate the transmission dynamics of COVID-19 taking the United States and China as examples. The Bass model was originated from business literature and initially modeled the process of new products getting adopted by the population with an external and internal influence term. First, we fit the cumulative number of confirmed COVID-19 cases in 8 major cities in the United States with the Bass model. The external and internal parameters of Bass were calculated and correlation analyses were performed between the parameters and the volume of traveling across different cities and within a city. The results show that the Bass model fits the epidemics data better than the logistic distribution which only has an internal influence term and the SIR model which is a classical infectious disease model. Besides, there is a significant positive correlation between the external parameter of Bass and the number of passengers at the airport as well as between the internal parameter of Bass and the number of short-distance trips in a city. Therefore, it is closer to true circumstances considering both external and internal transmission rather than assuming a region to be isolated. The external infection rate rises as the number of enplanements rises and the internal infection rate rises as the number of short-distance trips in a city rises. Second, we put forward an adapted multi-center Bass model for the multi-chain COVID-19 transmission in China and compared it with the original Bass model. The results indicated that the accuracy of the multi-center Bass model was higher than that of the original Bass model. In conclusion, the Bass model distinguishes the external and internal effects and is suitable for simulating the spread of COVID-19 and analyzing the infection rate caused by social interactions among different regions and inside a region. The adapted multi-center Bass model commendably described disease transmission when there is more than one transmission center. Our research proves the Bass model to be a useful tool for fine-level analyses on the transmission mechanism of COVID-19.
We used the Bass model to investigate the transmission dynamics of COVID-19 taking the United States and China as examples. The Bass model was originated from business literature and initially modeled the process of new products getting adopted by the population with an external and internal influence term. First, we fit the cumulative number of confirmed COVID-19 cases in 8 major cities in the United States with the Bass model. The external and internal parameters of Bass were calculated and correlation analyses were performed between the parameters and the volume of traveling across different cities and within a city. The results show that the Bass model fits the epidemics data better than the logistic distribution which only has an internal influence term and the SIR model which is a classical infectious disease model. Besides, there is a significant positive correlation between the external parameter of Bass and the number of passengers at the airport as well as between the internal parameter of Bass and the number of short-distance trips in a city. Therefore, it is closer to true circumstances considering both external and internal transmission rather than assuming a region to be isolated. The external infection rate rises as the number of enplanements rises and the internal infection rate rises as the number of short-distance trips in a city rises. Second, we put forward an adapted multi-center Bass model for the multi-chain COVID-19 transmission in China and compared it with the original Bass model. The results indicated that the accuracy of the multi-center Bass model was higher than that of the original Bass model. In conclusion, the Bass model distinguishes the external and internal effects and is suitable for simulating the spread of COVID-19 and analyzing the infection rate caused by social interactions among different regions and inside a region. The adapted multi-center Bass model commendably described disease transmission when there is more than one transmission center. Our research proves the Bass model to be a useful tool for fine-level analyses on the transmission mechanism of COVID-19.
G. Cacciapaglia, C. Cot, and F. Sannino, Second wave COVID-19 pandemics in Europe: A temporal playbook, Sci. Rep., vol. 10, no. 1, p. 15514, 2020.
P. D. Waggoner, Pandemic policymaking, Journal of Social Computing, vol. 2, no. 1, pp. 14–26, 2021.
I. Cooper, A. Mondal, and C. G. Antonopoulos, A SIR model assumption for the spread of COVID-19 in different communities, Chaos,Solitons&Fractals, vol. 139, p. 110057, 2020.
Y. -C. Chen, P. -E. Lu, C. -S. Chang, and T. -H. Liu, A time-dependent SIR model for COVID-19 with undetectable infected persons, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 4, pp. 3279–3294, 2020.
Y. Zeng, X. Guo, Q. Deng, S. Luo, and H. Zhang, Forecasting of COVID-19: Spread with dynamic transmission rate, J. Saf. Sci. Resil., vol. 1, no. 2, pp. 91–96, 2020.
E. Eryarsoy, D. Delen, B. Davazdahemami, and K. Topuz, A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19, J. Bus. Res., vol. 124, pp. 163–178, 2021.
Y. Wei, J. Wang, W. Song, C. Xiu, L. Ma, and T. Pei, Spread of COVID-19 in China: Analysis from a city-based epidemic and mobility model, Cities, vol. 110, p. 103010, 2021.
J. S. Jia, X. Lu, Y. Yuan, G. Xu, J. Jia, and N. A. Christakis, Population flow drives spatio-temporal distribution of COVID-19 in China, Nature, vol. 582, no. 7812, pp. 389–394, 2020.
S. Tyrovolas, I. Giné-Vázquez, D. Fernández, M. Morena, A. Koyanagi, M. Janko, J. M. Haro, Y. Lin, P. Lee, W. Pan, et al., Estimating the COVID-19 spread through real-time population mobility patterns: Surveillance in low-and middle-income countries, J. Med. Internet Res., vol. 23, no. 6, p. e22999, 2021.
F. M. Bass, A new product growth for model consumer durables, Manage. Sci., vol. 15, no. 5, pp. 215–227, 1969.
Z. L. Maureal, J. M. Lapate, M. S. Dumandan, V. K. Bicar, and D. N. Gaylo, Adapted bass diffusion model for the spread of COVID-19 in the Philippines: Implications to interventions and flattening the curve, Int. J. Innov. Creat. Change, vol. 14, no. 3, pp. 1418–1437, 2020.
P. D. Waggoner, R. Y. Shapiro, S. Frederick, and M. Gong, Uncovering the online social structure surrounding COVID-19, Journal of Social Computing, vol. 2, no. 2, pp. 157–165, 2021.
This work was supported by the National Key R&D Program of China (No. 2021ZD0111200), National Science Foundation of China (Nos. 72174099 and 72042010), and High-tech Discipline Construction Fundings for Universities in Beijing (Safety Science and Engineering).
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).