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

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          Abstract

          The ongoing COVID-19 outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions have been 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 use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. Following 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 Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Na Zhu, Dingyu Zhang, Wenling Wang … (2020)
          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            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

            Zunyou Wu, Jennifer M. McGoogan (2020)
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              Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

              Qun Li, Xuhua Guan, Peng Wu … (2020)
              Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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                Author and article information

                Journal
                Journal ID (nlm-ta): Science
                Journal ID (iso-abbrev): Science
                Journal ID (publisher-id): SCIENCE
                Title: Science (New York, N.y.)
                Publisher: American Association for the Advancement of Science
                ISSN (Print): 0036-8075
                ISSN (Electronic): 1095-9203
                Publication date (Electronic): 25 March 2020
                Electronic Location Identifier: eabb4218
                Affiliations
                [1 ]Department of Zoology, University of Oxford, Oxford, UK,
                [2 ]Harvard Medical School, Harvard University, Boston, MA, USA.
                [3 ]Boston Children’s Hospital, Boston, MA, USA.
                [4 ]Network Science Institute, Northeastern University, Boston, MA, USA.
                [5 ]School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
                [6 ]Mathematical Sciences, University of Southampton, Southampton, UK.
                [7 ]Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, WA, USA.
                [8 ]Harvard T.H. Chan School of Public Health, Boston, MA, USA.
                [9 ]Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
                [10 ]Sorbonne Universite, Paris, France.
                [11 ]ISI Foundation, Turin, Italy.
                [12 ]State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
                [13 ]Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK.
                Author notes
                [†]

                Members of the Open COVID-19 Data Working Group are listed in the supplementary materials.

                Article
                Publisher ID: abb4218
                DOI: 10.1126/science.abb4218
                PMC ID: 7146642
                PubMed ID: 32213647
                SO-VID: 936b1e8a-b1a3-4772-a98f-1d025771efb0
                Copyright © Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works
                License:

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Date received : 03 March 2020
                Date accepted : 23 March 2020
                Date: 25 March 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/501100004211, Oxford Martin School, University of Oxford;
                Categories
                Subject: Research Article
                Subject: Research Articles
                Subject: R-Articles
                Subject: Ecology
                Subject: Epidemiology
                Subject: Coronavirus

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                ScienceOpen disciplines: Uncategorized

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