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Journal of Social Computing 2022, 3(2): 171-181 https://doi.org/10.23919/JSC.2022.0002
Open Access | Issue | Published: 01 June 2022

Modeling the External, Internal, and Multi-Center Transmission of Infectious Diseases: The COVID-19 Case

Show Author's Information Xiaojing Guo1,  Hui Zhang1( ),  Luyao Kou1,  Yufan Hou2
Institute of Public Safety Research, Tsinghua University, Beijing 100084, China
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
COVID-19, correlation analysis, diffusion models, epidemics
Cite this article:
Guo X, Zhang H, Kou L, et al. Modeling the External, Internal, and Multi-Center Transmission of Infectious Diseases: The COVID-19 Case. Journal of Social Computing, 2022, 3(2): 171-181. https://doi.org/10.23919/JSC.2022.0002
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Abstract Full text About this article

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.


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About this article

Modeling the External, Internal, and Multi-Center Transmission of Infectious Diseases: The COVID-19 Case

Show Author's information Xiaojing Guo1, Hui Zhang1( ), Luyao Kou1, Yufan Hou2
Institute of Public Safety Research, Tsinghua University, Beijing 100084, China
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Abstract

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.

Keywords: COVID-19, correlation analysis, diffusion models, epidemics

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Publication history

Received: 14 March 2022
Revised: 22 April 2022
Accepted: 04 May 2022
Published: 01 June 2022
Issue date: June 2022

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© The author(s) 2022

Acknowledgements

Acknowledgment

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).

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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/).

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