The effect of human mobility and control measures on the COVID-19 epidemic in China

Tracing infection from mobility data What sort of measures are required to contain the spread of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19)? The rich data from the Open COVID-19 Data Working Group include the dates when people first reported symptoms, not just a positive test date. Using these data and real-time travel data from the internet services company Baidu, Kraemer et al. found that mobility statistics offered a precise record of the spread of SARS-CoV-2 among the cities of China at the start of 2020. The frequency of introductions from Wuhan were predictive of the size of the epidemic sparked in other provinces. However, once the virus had escaped Wuhan, strict local control measures such as social isolation and hygiene, rather than long-distance travel restrictions, played the largest part in controlling SARS-CoV-2 spread. Science, this issue p. 493 Mobile phone data show that the spread of COVID-19 in China was driven by travel and mitigated substantially by local control measures. The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  C. Dolea,et al.  World Health Organization , 1949, International Organization.

[3]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[4]  Thomas de Quincey.  [C] , 2000, The Works of Thomas De Quincey, Vol. 1: Writings, 1799–1820.

[5]  B T Grenfell,et al.  Individual-based perspectives on R(0). , 2000, Journal of theoretical biology.

[6]  Martina Mittlböck,et al.  Pseudo R-squared measures for Poisson regression models with over- or underdispersion , 2003, Comput. Stat. Data Anal..

[7]  Oscar E. Gaggiotti,et al.  Ecology, genetics, and evolution of metapopulations , 2004 .

[8]  O. Bjørnstad,et al.  Metapopulation Dynamics of Infectious Diseases , 2004 .

[9]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[10]  C. Fraser,et al.  Factors that make an infectious disease outbreak controllable. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[11]  D. Watts,et al.  Multiscale, resurgent epidemics in a hierarchical metapopulation model. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[12]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[13]  Mark A. Miller,et al.  Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza , 2006, Science.

[14]  A. Zeileis,et al.  Regression Models for Count Data in R , 2008 .

[15]  R. Brookmeyer,et al.  Incubation periods of acute respiratory viral infections: a systematic review , 2009, The Lancet Infectious Diseases.

[16]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[17]  M. McHugh,et al.  The Chi-square test of independence , 2013, Biochemia medica.

[18]  Simon Cauchemez,et al.  Edinburgh Research Explorer Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility , 2022 .

[19]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[20]  M. Lipsitch,et al.  Temporally Varying Relative Risks for Infectious Diseases , 2016 .

[21]  C. Viboud,et al.  Mathematical models to characterize early epidemic growth: A review. , 2016, Physics of life reviews.

[22]  Trevor Hastie,et al.  Generalized Linear Models , 2017 .

[23]  M. Lipsitch,et al.  Temporally Varying Relative Risks for Infectious Diseases: Implications for Infectious Disease Control. , 2017, Epidemiology.

[24]  U. Obolski,et al.  Genomic and epidemiological monitoring of yellow fever virus transmission potential , 2018, Science.

[25]  S. Hay,et al.  A database of geopositioned Middle East Respiratory Syndrome Coronavirus occurrences , 2019, Scientific Data.

[26]  David L. Smith,et al.  Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus , 2019, Nature Microbiology.

[27]  Yongli Cai,et al.  Quantifying the association between domestic travel and the exportation of novel coronavirus (2019-nCoV) cases from Wuhan, China in 2020: a correlational analysis , 2020, Journal of travel medicine.

[28]  John S. Brownstein,et al.  Epidemiological data from the COVID-19 outbreak, real-time case information , 2020, Scientific Data.

[29]  Ruifu Yang,et al.  The impact of transmission control measures during the first 50 days of the COVID-19 epidemic in China , 2020, medRxiv.

[30]  S. Lo,et al.  A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster , 2020, The Lancet.

[31]  S. Duan,et al.  The impact of traffic isolation in Wuhan on the spread of 2019-nCov , 2020, medRxiv.

[32]  Nuno R. Faria,et al.  The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, medRxiv.

[33]  L. Meyers,et al.  Risk for Transportation of Coronavirus Disease from Wuhan to Other Cities in China , 2020, Emerging infectious diseases.

[34]  C. Althaus,et al.  Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[35]  Ruiyun Li,et al.  Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2) , 2020, Science.

[36]  Ruiyun Li,et al.  Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19) , 2020, medRxiv.

[37]  中国疾病预防控制中心新型冠状病毒肺炎应急响应机制流行病学组.  The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China./ 新型冠状病毒肺炎流行病学特征分析 , 2020 .

[38]  Sumiko Mekaru,et al.  Open access epidemiological data from the COVID-19 outbreak , 2020, The Lancet Infectious Diseases.

[39]  Novel Coronavirus Pneumonia Emergency Response Epidemiol Team.  [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. , 2020, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[40]  D. Fisman,et al.  Reporting, Epidemic Growth, and Reproduction Numbers for the 2019 Novel Coronavirus (2019-nCoV) Epidemic , 2020, Annals of Internal Medicine.

[41]  G. Leung,et al.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.

[42]  Hualiang Lin,et al.  Population movement, city closure and spatial transmission of the 2019-nCoV infection in China , 2020, medRxiv.

[43]  Don Klinkenberg,et al.  Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20–28 January 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[44]  Zunyou Wu,et al.  Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. , 2020, JAMA.

[45]  Simon Cauchemez,et al.  Risk for Transportation of 2019 Novel Coronavirus Disease from Wuhan to Other Cities in China , 2020 .

[46]  Jing Zhao,et al.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia , 2020, The New England journal of medicine.

[47]  Weizhong Yang,et al.  COVID-19 control in China during mass population movements at New Year , 2020, The Lancet.

[48]  Ruifu Yang,et al.  An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China , 2020, Science.

[49]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.