Analyzing the effects of public interventions on reducing public gatherings in China during the COVID-19 epidemic via mobile terminals positioning data

Lei Nie; Xin Guo; Chengqi Yi; Ruojia Wang; Lei Nie; Xin Guo; Chengqi Yi; Ruojia Wang

doi:10.3934/mbe.2020265

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 5: 4875-4890. doi: 10.3934/mbe.2020265

Previous Article Next Article

Research article Special Issues

Analyzing the effects of public interventions on reducing public gatherings in China during the COVID-19 epidemic via mobile terminals positioning data

1.
Department of Information Management, Peking University, Beijing, 100871, China
2.
Department of Big Data Development, State Information Center, Beijing, 100045, China

Received: 07 May 2020 Accepted: 09 July 2020 Published: 13 July 2020

At the beginning of 2020, the novel coronavirus disease (COVID-19) became an outbreak in China. On January 23, China raised its national public health response to the highest level. As part of the emergency response, a series of public social distancing interventions were implemented to reduce the transmission rate of COVID-19. In this article, we explored the feasibility of using mobile terminal positioning data to study the impact of some nonpharmaceutical public health interventions implemented by China. First, this article introduced a hybrid method for measuring the number of people in public places based on anonymized mobile terminal positioning data. Additionally, the difference-in-difference (DID) model was used to estimate the effect of the interventions on reducing public gatherings in different provinces and during different stages. The data-driven experimental results showed that the interventions that China implemented reduced the number of people in public places by approximately 60% between January 24 and February 28. Among the 31 provinces in the Chinese mainland, some provinces, such as Tianjin and Chongqing, were more affected by the interventions, while other provinces, such as Gansu, were less affected. In terms of the stages, the phase with the greatest intervention effect was from February 3 to 14, during which the number of daily confirmed cases in China showed a turning point. In conclusion, the interventions significantly reduced public gatherings, and the effects of interventions varied with provinces and time.
- COVID-19,
- effect evaluation,
- public gatherings,
- mobile terminals positioning data,
- difference-in-difference model
Citation: Lei Nie, Xin Guo, Chengqi Yi, Ruojia Wang. Analyzing the effects of public interventions on reducing public gatherings in China during the COVID-19 epidemic via mobile terminals positioning data[J]. Mathematical Biosciences and Engineering, 2020, 17(5): 4875-4890. doi: 10.3934/mbe.2020265

Related Papers:

Abstract

At the beginning of 2020, the novel coronavirus disease (COVID-19) became an outbreak in China. On January 23, China raised its national public health response to the highest level. As part of the emergency response, a series of public social distancing interventions were implemented to reduce the transmission rate of COVID-19. In this article, we explored the feasibility of using mobile terminal positioning data to study the impact of some nonpharmaceutical public health interventions implemented by China. First, this article introduced a hybrid method for measuring the number of people in public places based on anonymized mobile terminal positioning data. Additionally, the difference-in-difference (DID) model was used to estimate the effect of the interventions on reducing public gatherings in different provinces and during different stages. The data-driven experimental results showed that the interventions that China implemented reduced the number of people in public places by approximately 60% between January 24 and February 28. Among the 31 provinces in the Chinese mainland, some provinces, such as Tianjin and Chongqing, were more affected by the interventions, while other provinces, such as Gansu, were less affected. In terms of the stages, the phase with the greatest intervention effect was from February 3 to 14, during which the number of daily confirmed cases in China showed a turning point. In conclusion, the interventions significantly reduced public gatherings, and the effects of interventions varied with provinces and time.

References

[1]	D. S. Hui, E. I. Azhar, T. A Madani, F. Ntoumi, R. Kock, O. Dar, et al., The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health-The latest 2019 novel coronavirus outbreak in Wuhan, China, Int. J. Infect. Dis., 91 (2020), 264-266.
[2]	H. Lau, V. Khosrawipour, P. Kocbach, A. Mikolajczyk, J. Schubert, J. Bania, et al., The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China, J. Travel Med., 27 (2020), taaa037.
[3]	Z. L. Chen, Q. Zhang, Y. Lu, Z. Guo, X. Zhang, W. Zhang, et al., Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China, Chin. Med. J., 133 (2020), 1044-1050.
[4]	M. U. Kraemer, C. H. Yang, B. Gutierrez, C. H. Wu, B. Klein, D. M. Pigott, et al., The effect of human mobility and control measures on the COVID-19 epidemic in China, Science, 368 (2020), 493-497.
[5]	C. R. Wells, P. Sah, S. M. Moghadas, A. Pandey, A. Shoukat, Y. Wang, et al., Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak, Proc. Natl. Acad. Sci. U.S.A., 117 (2020), 7504-7509.
[6]	M. Chinazzi, J. T. Davis, M. Ajelli, C. Gioannini, M. Litvinova, S. Merler, et al., The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak, Science, 368 (2020), 395-400.
[7]	A Pan, L Liu, C Wang, H. Guo, X. Hao, Q. Wang, et al., Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China, JAMA, 323 (2020), 1915-1923.
[8]	K. Yang, L Wang, F Li, D. Chen, X. Li, C. Qiu, et al., The influence of preventive strategies on COVID-2019 epidemic in Shenzhen, China, Eur. Respir. J., 55 (2020), 2000599.
[9]	H. Sjödin, A. Wilder-Smith, S. Osman, Z. Farooq, J. Rocklov, Only strict quarantine measures can curb the coronavirus disease (COVID-19) outbreak in Italy, 2020, Euro Surveill., 25 (2020), 2000280.
[10]	C. O. Buckee, S. Balsari, J. Chan, M. Crosas, F. Dominici, U. Gasser, et al., Aggregated mobility data could help fight COVID-19, Science, 368 (2020), 145-146.
[11]	A. Lasry, D. Kidder, M. Hast, J. Poovey, G. Sunshine, K. Winglee, et al., Timing of Community Mitigation and Changes in Reported COVID-19 and Community Mobility - Four U.S. Metropolitan Areas, February 26-April 1, 2020, Morb. Mortal. Wkly. Rep., 69 (2020), 451-457.
[12]	B. D Meyer, Natural and quasi-experiments in economics, J. Bus. Econ. Stat., 13 (1995), 151-161.
[13]	H. White, A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica, 48 (1980), 817-838.
[14]	W. K. Newey, K. D West, A. Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica, 55 (1987), 703-708.
[15]	S. A. Lauer, K. H. Grantz, Q. Bi, F. Jones, Q. Zheng, H. Meredith, et al., The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application, Ann. Intern. Med., 172 (2020), 577-582.

Reader Comments

your name:*

Email:*
© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)