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      An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China

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          Responding to an outbreak of a novel coronavirus (agent of COVID-19) in December 2019, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. We investigated the spread and control of COVID-19 using a unique data set including case reports, human movement and public health interventions. The Wuhan shutdown was associated with the delayed arrival of COVID-19 in other cities by 2.91 days (95%CI: 2.54-3.29). Cities that implemented control measures pre-emptively reported fewer cases, on average, in the first week of their outbreaks (13.0; 7.1-18.8) compared with cities that started control later (20.6; 14.5-26.8). Suspending intra-city public transport, closing entertainment venues and banning public gatherings were associated with reductions in case incidence. The national emergency response appears to have delayed the growth and limited the size of the COVID-19 epidemic in China, averting hundreds of thousands of cases by 19 February (day 50).

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          Spatial population dynamics: analyzing patterns and processes of population synchrony.

          The search for mechanisms behind spatial population synchrony is currently a major issue in population ecology. Theoretical studies highlight how synchronizing mechanisms such as dispersal, regionally correlated climatic variables and mobile enemies might interact with local dynamics to produce different patterns of spatial covariance. Specialized statistical methods, applied to large-scale survey data, aid in testing the theoretical predictions with empirical estimates. Observational studies and experiments on the demography of local populations are paramount to identify the true ecological mechanisms. The recent achievements illustrate the power of combining theory, observation and/or experimentation and statistical modeling in the ecological research protocol.
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            Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

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              Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China

              There is increasing concern about another influenza pandemic in China. However, the understanding of the roles of transport modes in the 2009 influenza A(H1N1) pandemic spread across mainland China is limited. Herein, we collected 127,797 laboratory-confirmed cases of influenza A(H1N1)pdm09 in mainland China from May 2009 to April 2010. Arrival days and peak days were calculated for all 340 prefectures to characterize the dissemination patterns of the pandemic. We first evaluated the effects of airports and railway stations on arrival days and peak days, and then we applied quantile regressions to quantify the relationships between arrival days and air, rail, and road travel. Our results showed that early arrival of the virus was not associated with an early incidence peak. Airports and railway stations in prefectures significantly advanced arrival days but had no significant impact on peak days. The pandemic spread across mainland China from the southeast to the northwest in two phases that were split at approximately 1 August 2009. Both air and road travel played a significant role in accelerating the spread during phases I and II, but rail travel was only significant during phase II. In conclusion, in addition to air and road travel, rail travel also played a significant role in accelerating influenza A(H1N1)pdm09 spread between prefectures. Establishing a multiscale mobility network that considers the competitive advantage of rail travel for mid to long distances is essential for understanding the influenza pandemic transmission in China.

                Author and article information

                Science (New York, N.y.)
                American Association for the Advancement of Science
                31 March 2020
                [1 ]State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
                [2 ]Department of Zoology, University of Oxford, Oxford, UK.
                [3 ]Mathematical Sciences, University of Southampton, Southampton, UK.
                [4 ]Department of Land, Air and Water Resources, University of California Davis, CA, USA.
                [5 ]Harvard Medical School, Harvard University, Boston, MA, USA.
                [6 ]Boston Children’s Hospital, Boston, MA, USA.
                [7 ]Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China.
                [8 ]Beijing Center for Disease Prevention and Control, Beijing, China.
                [9 ]State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
                [10 ]Department of Urban Planning and Design, The University of Hong Kong, Hong Kong.
                [11 ]Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, China.
                [12 ]Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, Pennsylvania, USA.
                [13 ]Department of Entomology, College of Agricultural Sciences, Pennsylvania State University, University Park, Pennsylvania, USA.
                [14 ]Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
                [15 ]Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
                Author notes

                These authors contributed equally to this work.

                Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

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

                Funded by: doi http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81673234
                Funded by: Young Elite Scientist Sponsorship Program by CAST (YESS);
                Award ID: 2018QNRC001
                Funded by: Beijing Natural Science Foundation;
                Award ID: JQ18025
                Funded by: Beijing Advanced Innovation Program for Land Surface Science;
                Award ID: None
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