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Analyzing the effects of public interventions on reducing public gatherings in China during the COVID-19 epidemic via mobile terminals positioning data.
Math Biosci Eng. 2020 07 13; 17(5):4875-4890.MB

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.

Authors+Show Affiliations

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

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

33120532

Citation

Nie, Lei, et al. "Analyzing the Effects of Public Interventions On Reducing Public Gatherings in China During the COVID-19 Epidemic Via Mobile Terminals Positioning Data." Mathematical Biosciences and Engineering : MBE, vol. 17, no. 5, 2020, pp. 4875-4890.
Nie L, Guo X, Yi CQ, et al. Analyzing the effects of public interventions on reducing public gatherings in China during the COVID-19 epidemic via mobile terminals positioning data. Math Biosci Eng. 2020;17(5):4875-4890.
Nie, L., Guo, X., Yi, C. Q., & Wang, R. J. (2020). Analyzing the effects of public interventions on reducing public gatherings in China during the COVID-19 epidemic via mobile terminals positioning data. Mathematical Biosciences and Engineering : MBE, 17(5), 4875-4890. https://doi.org/10.3934/mbe.2020265
Nie L, et al. Analyzing the Effects of Public Interventions On Reducing Public Gatherings in China During the COVID-19 Epidemic Via Mobile Terminals Positioning Data. Math Biosci Eng. 2020 07 13;17(5):4875-4890. PubMed PMID: 33120532.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Analyzing the effects of public interventions on reducing public gatherings in China during the COVID-19 epidemic via mobile terminals positioning data. AU - Nie,Lei, AU - Guo,Xin, AU - Yi,Cheng Qi, AU - Wang,Ruo Jia, PY - 2020/10/30/entrez PY - 2020/10/31/pubmed PY - 2020/11/18/medline KW - COVID-19 KW - difference-in-difference model KW - effect evaluation KW - mobile terminals positioning data KW - public gatherings SP - 4875 EP - 4890 JF - Mathematical biosciences and engineering : MBE JO - Math Biosci Eng VL - 17 IS - 5 N2 - 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. SN - 1551-0018 UR - https://www.unboundmedicine.com/medline/citation/33120532/Analyzing_the_effects_of_public_interventions_on_reducing_public_gatherings_in_China_during_the_COVID_19_epidemic_via_mobile_terminals_positioning_data_ L2 - https://www.aimspress.com/article/10.3934/mbe.2020265 DB - PRIME DP - Unbound Medicine ER -