This page has only limited features, please log in for full access.
Mobility restrictions have been a heated topic during the global pandemic of coronavirus disease 2019 (COVID-19). However, multiple recent findings have verified its importance in blocking virus spread. Evidence on the association between mobility, cases imported from abroad and local medical resource supplies is limited. To reveal the association, this study quantified the importance of inter- and intra-country mobility in containing virus spread and avoiding hospitalizations during early stages of COVID-19 outbreaks in India, Japan, and China. We calculated the time-varying reproductive number (R t) and duration from illness onset to diagnosis confirmation (D oc), to represent conditions of virus spread and hospital bed shortages, respectively. Results showed that inter-country mobility fluctuation could explain 80%, 35%, and 12% of the variance in imported cases and could prevent 20 million, 5 million, and 40 million imported cases in India, Japan and China, respectively. The critical time for screening and monitoring of imported cases is 2 weeks at minimum and 4 weeks at maximum, according to the time when the Pearson’s Rs between R t and imported cases reaches a peak (>0.8). We also found that if local transmission is initiated, a 1% increase in intra-country mobility would result in 1430 (±501), 109 (±181), and 10 (±1) additional bed shortages, as estimated using the D oc in India, Japan, and China, respectively. Our findings provide vital reference for governments to tailor their pre-vaccination policies regarding mobility, especially during future epidemic waves of COVID-19 or similar severe epidemic outbreaks.
Zhehao Ren; Ruiyun Li; Tao Zhang; Bin Chen; Che Wang; Miao Li; Shuang Song; Yixiong Xiao; Bo Xu; Zhaoyang Liu; Chong Shen; Dabo Guan; Lin Hou; Ke Deng; Yuqi Bai; Peng Gong; Bing Xu. Reduction of Human Mobility Matters during Early COVID-19 Outbreaks: Evidence from India, Japan and China. International Journal of Environmental Research and Public Health 2021, 18, 2826 .
AMA StyleZhehao Ren, Ruiyun Li, Tao Zhang, Bin Chen, Che Wang, Miao Li, Shuang Song, Yixiong Xiao, Bo Xu, Zhaoyang Liu, Chong Shen, Dabo Guan, Lin Hou, Ke Deng, Yuqi Bai, Peng Gong, Bing Xu. Reduction of Human Mobility Matters during Early COVID-19 Outbreaks: Evidence from India, Japan and China. International Journal of Environmental Research and Public Health. 2021; 18 (6):2826.
Chicago/Turabian StyleZhehao Ren; Ruiyun Li; Tao Zhang; Bin Chen; Che Wang; Miao Li; Shuang Song; Yixiong Xiao; Bo Xu; Zhaoyang Liu; Chong Shen; Dabo Guan; Lin Hou; Ke Deng; Yuqi Bai; Peng Gong; Bing Xu. 2021. "Reduction of Human Mobility Matters during Early COVID-19 Outbreaks: Evidence from India, Japan and China." International Journal of Environmental Research and Public Health 18, no. 6: 2826.
Emerging evidence suggests a resurgence of COVID-19 in the coming years. It is thus critical to optimize emergency response planning from a broad, integrated perspective. We developed a mathematical model incorporating climate-driven variation in community transmissions and movement-modulated spatial diffusions of COVID-19 into various intervention scenarios. We find that an intensive 8-wk intervention targeting the reduction of local transmissibility and international travel is efficient and effective. Practically, we suggest a tiered implementation of this strategy where interventions are first implemented at locations in what we call the Global Intervention Hub, followed by timely interventions in secondary high-risk locations. We argue that thinking globally, categorizing locations in a hub-and-spoke intervention network, and acting locally, applying interventions at high-risk areas, is a functional strategy to avert the tremendous burden that would otherwise be placed on public health and society.
Ruiyun Li; Bin Chen; Tao Zhang; Zhehao Ren; Yimeng Song; Yixiong Xiao; Lin Hou; Jun Cai; Bo Xu; Miao Li; Karen Kie Yan Chan; Ying Tu; Mu Yang; Jing Yang; Zhaoyang Liu; Chong Shen; Che Wang; Lei Xu; Qiyong Liu; Shuming Bao; Jianqin Zhang; Yuhai Bi; Yuqi Bai; Ke Deng; Wusheng Zhang; Wenyu Huang; Jason D. Whittington; Nils Chr. Stenseth; Dabo Guan; Peng Gong; Bing Xu. Global COVID-19 pandemic demands joint interventions for the suppression of future waves. Proceedings of the National Academy of Sciences 2020, 117, 26151 -26157.
AMA StyleRuiyun Li, Bin Chen, Tao Zhang, Zhehao Ren, Yimeng Song, Yixiong Xiao, Lin Hou, Jun Cai, Bo Xu, Miao Li, Karen Kie Yan Chan, Ying Tu, Mu Yang, Jing Yang, Zhaoyang Liu, Chong Shen, Che Wang, Lei Xu, Qiyong Liu, Shuming Bao, Jianqin Zhang, Yuhai Bi, Yuqi Bai, Ke Deng, Wusheng Zhang, Wenyu Huang, Jason D. Whittington, Nils Chr. Stenseth, Dabo Guan, Peng Gong, Bing Xu. Global COVID-19 pandemic demands joint interventions for the suppression of future waves. Proceedings of the National Academy of Sciences. 2020; 117 (42):26151-26157.
Chicago/Turabian StyleRuiyun Li; Bin Chen; Tao Zhang; Zhehao Ren; Yimeng Song; Yixiong Xiao; Lin Hou; Jun Cai; Bo Xu; Miao Li; Karen Kie Yan Chan; Ying Tu; Mu Yang; Jing Yang; Zhaoyang Liu; Chong Shen; Che Wang; Lei Xu; Qiyong Liu; Shuming Bao; Jianqin Zhang; Yuhai Bi; Yuqi Bai; Ke Deng; Wusheng Zhang; Wenyu Huang; Jason D. Whittington; Nils Chr. Stenseth; Dabo Guan; Peng Gong; Bing Xu. 2020. "Global COVID-19 pandemic demands joint interventions for the suppression of future waves." Proceedings of the National Academy of Sciences 117, no. 42: 26151-26157.
Fine particulate matter (PM2.5) has been the focus of increasing public concerns because of its adverse effect on environment and health risks. However, existing efforts of mapping PM2.5 concentrations are always limited by coarse spatial resolutions and temporal frequencies. Addressing this shortcoming, here we explicitly estimated hourly PM2.5 concentrations at 1-km spatial resolution across China from March 2018 to February 2019 using a two-stage random forest model. In the first stage, we conducted a gap-filling method to generate full-coverage Aerosol Optical Depth (AOD) by fusing AOD data from satellite (Himawari-8 and MODIS) and weather forecast model (CAMS), and additional meteorological and geographical variables. Gap-filled AOD generated in Stage I was subsequently used to estimate hourly PM2.5 in Stage II. Results showed that our model achieved accurate and robust estimations of PM2.5 concentrations, with an overall cross-validated R2 of 0.85, root mean squared error of 11.02 μg/m3, and mean absolute error of 6.73 μg/m3. CAMS-simulated PM2.5, elevation, and gap-filled AOD were identified to be relatively important variables contributing to the model performance of PM2.5 estimation. The model performance varied over the daily temporal scale. Specifically, daily estimation model performed better in spring and winter but worse in summer and autumn. In this study, we provided an alternative to generate spatially and temporally explicit mapping of PM2.5 concentrations with fine resolutions, making it possible to achieve real-time monitoring of air pollutions. The detailed spatial heterogeneity and diurnal variability of PM2.5 concentrations will also be valuable and supportive for environmental exposure assessment and related policy-driven regulations.
Tingting Jiang; Bin Chen; Zhen Nie; Zhehao Ren; Bing Xu; Shihao Tang. Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model. Atmospheric Research 2020, 248, 105146 .
AMA StyleTingting Jiang, Bin Chen, Zhen Nie, Zhehao Ren, Bing Xu, Shihao Tang. Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model. Atmospheric Research. 2020; 248 ():105146.
Chicago/Turabian StyleTingting Jiang; Bin Chen; Zhen Nie; Zhehao Ren; Bing Xu; Shihao Tang. 2020. "Estimation of hourly full-coverage PM2.5 concentrations at 1-km resolution in China using a two-stage random forest model." Atmospheric Research 248, no. : 105146.
Nighttime light remote sensing has aroused great popularity because of its advantage in estimating socioeconomic indicators and quantifying human activities in response to the changing world. Despite many advances that have been made in method development and implementation of nighttime light remote sensing over the past decades, limited studies have dived into answering the question: Where does nighttime light come from? This hinders our capability of identifying specific sources of nighttime light in urbanized regions. Addressing this shortcoming, here we proposed a parcel-oriented temporal linear unmixing method (POTLUM) to identify specific nighttime light sources with the integration of land use data. Ratio of root mean square error was used as the measure to assess the unmixing accuracy, and parcel purity index and source sufficiency index were proposed to attribute unmixing errors. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light dataset from the Suomi National Polar-Orbiting Partnership (NPP) satellite and the newly released Essential Urban Land Use Categories in China (EULUC-China) product, we applied the proposed method and conducted experiments in two China cities with different sizes, Shanghai and Quzhou. Results of the POTLUM showed its relatively robust applicability of detecting specific nighttime light sources, achieving an rRMSE of 3.38% and 1.04% in Shanghai and Quzhou, respectively. The major unmixing errors resulted from using impure land parcels as endmembers (i.e., parcel purity index for Shanghai and Quzhou: 54.48%, 64.09%, respectively), but it also showed that predefined light sources are sufficient (i.e., source sufficiency index for Shanghai and Quzhou: 96.53%, 99.55%, respectively). The method presented in this study makes it possible to identify specific sources of nighttime light and is expected to enrich the estimation of structural socioeconomic indicators, as well as better support various applications in urban planning and management.
Zhehao Ren; Yufu Liu; Bin Chen; Bing Xu. Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution. Remote Sensing 2020, 12, 1922 .
AMA StyleZhehao Ren, Yufu Liu, Bin Chen, Bing Xu. Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution. Remote Sensing. 2020; 12 (12):1922.
Chicago/Turabian StyleZhehao Ren; Yufu Liu; Bin Chen; Bing Xu. 2020. "Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution." Remote Sensing 12, no. 12: 1922.
Soil organic carbon (SOC) is a key component of the global carbon cycle, yet it is not well-represented in Earth system models to accurately predict global carbon dynamics in response to climate change. This novel study integrated deep learning, data assimilation, 25,444 vertical soil profiles, and the Community Land Model version 5 (CLM5) to optimize the model representation of SOC over the conterminous United States. We firstly constrained parameters in CLM5 using observations of vertical profiles of SOC in both a batch mode (using all individual soil layers in one batch) and at individual sites (site-by-site). The estimated parameter values from the site-by-site data assimilation were then either randomly sampled (random-sampling) to generate continentally homogeneous (constant) parameter values or maximally preserved for their spatially heterogeneous distributions (varying parameter values to match the spatial patterns from the site-by-site data assimilation) so as to optimize spatial representation of SOC in CLM5 through a deep learning technique (neural networking) over the conterminous United States. Comparing modeled spatial distributions of SOC by CLM5 to observations yielded increasing predictive accuracy from default CLM5 settings (R2 = 0.32) to randomly sampled (0.36), one-batch estimated (0.43), and deep learning optimized (0.62) parameter values. While CLM5 with parameter values derived from random-sampling and one-batch methods substantially corrected the overestimated SOC storage by that with default model parameters, there were still considerable geographical biases. CLM5 with the spatially heterogeneous parameter values optimized from the neural networking method had the least estimation error and less geographical biases across the conterminous United States. Our study indicated that deep learning in combination with data assimilation can significantly improve the representation of SOC by complex land biogeochemical models.
Feng Tao; Zhenghu Zhou; Yuanyuan Huang; Qianyu Li; Xingjie Lu; Shuang Ma; Xiaomeng Huang; Yishuang Liang; Gustaf Hugelius; Lifen Jiang; Russell Doughty; Zhehao Ren; Yiqi Luo. Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States. Frontiers in Big Data 2020, 3, 1 .
AMA StyleFeng Tao, Zhenghu Zhou, Yuanyuan Huang, Qianyu Li, Xingjie Lu, Shuang Ma, Xiaomeng Huang, Yishuang Liang, Gustaf Hugelius, Lifen Jiang, Russell Doughty, Zhehao Ren, Yiqi Luo. Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States. Frontiers in Big Data. 2020; 3 ():1.
Chicago/Turabian StyleFeng Tao; Zhenghu Zhou; Yuanyuan Huang; Qianyu Li; Xingjie Lu; Shuang Ma; Xiaomeng Huang; Yishuang Liang; Gustaf Hugelius; Lifen Jiang; Russell Doughty; Zhehao Ren; Yiqi Luo. 2020. "Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States." Frontiers in Big Data 3, no. : 1.
The World Climate Research Programme (WCRP) facilitates analysis and prediction of Earth system change for use in a range of practical applications of direct relevance, benefit and value to society. WCRP initialized the Coupled Model Intercomparison Project (CMIP) in 1995. The aim of CMIP is to better understand past, present and future climate changes arising from natural, unforced variability or in response to changes in radiative forcing in a multi-model context.
The climate model output data that are being produced during this sixth phase of CMIP (CMIP6) is expected to be 40~60 PB. It is still not very clear whether researchers worldwide may experience a big problem when downloading such a huge volume of data. This work addressed this issue by performing data download speed test for all the CMIP6 data nodes.
A Google Chrome-based data download speed test website (http://speedtest.theropod.tk) was implemented. It leverages the Allow CORS: Access-Control-Allow-Origin extension to access to each CMIP6 data node. This test consists of four steps: Installing and enabling Allow CORS extension in Chrome, performing data download speed test for all the CMIP6 data nodes, presenting the test results, and uninstalling the extension. The speed test is performed by downloading a certain chunk of model output data file from the thredds data server of each data node.
Researchers from 11 countries have performed this test in 24 cities against all the 26 CMIP6 data nodes. The fastest transfer speed was 124MB/s, and the slowest were 0 MB/s because of connect timeout. Data transfer speed in developed countries (United States, Netherland, Japan, Canada, Great Britain) is significantly faster than that in developing countries (China, India, Russia, Pakistan). In developed countries the data transfer mean speed is roughly 80Mb/s, equal to the median US residential broadband speed provided by cable or fiber(FCC Measuring Fixed Broadband - Eighth Report, but in developing countries the mean transfer speed is usually much slower, roughly 9Mb/s. Data transfer speed was significantly faster when the data nodes and test sites were both at developed countries, for example, downloading data from IPSL, DKRZ or GFDL at Wolvercote, UK.
Although further test are definitely needed, this preliminary result clearly show that the actual data download speed varies dramatically in different countries, and for different data node. This suggests that ensuring smooth access to CMIP6 data is still challenging.
Yufu Liu; Zhehao Ren; Karen K.Y. Chan; Yuqi Bai. Data download speed test for CMIP6 model output: preliminary results. 2020, 1 .
AMA StyleYufu Liu, Zhehao Ren, Karen K.Y. Chan, Yuqi Bai. Data download speed test for CMIP6 model output: preliminary results. . 2020; ():1.
Chicago/Turabian StyleYufu Liu; Zhehao Ren; Karen K.Y. Chan; Yuqi Bai. 2020. "Data download speed test for CMIP6 model output: preliminary results." , no. : 1.
Peng Gong; Bin Chen; Xuecao Li; Han Liu; Jie Wang; Yuqi Bai; Jingming Chen; Xi Chen; Lei Fang; Shuailong Feng; Yongjiu Feng; Yali Gong; Hao Gu; Huabing Huang; Xiaochun Huang; Hongzan Jiao; Yingdong Kang; Guangbin Lei; Ainong Li; Xiaoting Li; Xun Li; Yuechen Li; Zhilin Li; Zhongde Li; Chong Liu; Chunxia Liu; Maochou Liu; Shuguang Liu; Wanliu Mao; Changhong Miao; Hao Ni; Qisheng Pan; Shuhua Qi; Zhehao Ren; Zhuoran Shan; Shaoqing Shen; Minjun Shi; Yimeng Song; Mo Su; Hoi Ping Suen; Bo Sun; Fangdi Sun; Jian Sun; Lin Sun; Wenyao Sun; Tian Tian; Xiaohua Tong; Yihsing Tseng; Ying Tu; Hong Wang; Lan Wang; Xi Wang; Zongming Wang; Tinghai Wu; Yaowen Xie; Jian Yang; Jun Yang; Man Yuan; Wenze Yue; Hongda Zeng; Kuo Zhang; Neng Zhang; Tao Zhang; Yu Zhang; Feng Zhao; Yichen Zheng; Qiming Zhou; Nicholas Clinton; Zhiliang Zhu; Bing Xu. Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. Science Bulletin 2019, 65, 182 -187.
AMA StylePeng Gong, Bin Chen, Xuecao Li, Han Liu, Jie Wang, Yuqi Bai, Jingming Chen, Xi Chen, Lei Fang, Shuailong Feng, Yongjiu Feng, Yali Gong, Hao Gu, Huabing Huang, Xiaochun Huang, Hongzan Jiao, Yingdong Kang, Guangbin Lei, Ainong Li, Xiaoting Li, Xun Li, Yuechen Li, Zhilin Li, Zhongde Li, Chong Liu, Chunxia Liu, Maochou Liu, Shuguang Liu, Wanliu Mao, Changhong Miao, Hao Ni, Qisheng Pan, Shuhua Qi, Zhehao Ren, Zhuoran Shan, Shaoqing Shen, Minjun Shi, Yimeng Song, Mo Su, Hoi Ping Suen, Bo Sun, Fangdi Sun, Jian Sun, Lin Sun, Wenyao Sun, Tian Tian, Xiaohua Tong, Yihsing Tseng, Ying Tu, Hong Wang, Lan Wang, Xi Wang, Zongming Wang, Tinghai Wu, Yaowen Xie, Jian Yang, Jun Yang, Man Yuan, Wenze Yue, Hongda Zeng, Kuo Zhang, Neng Zhang, Tao Zhang, Yu Zhang, Feng Zhao, Yichen Zheng, Qiming Zhou, Nicholas Clinton, Zhiliang Zhu, Bing Xu. Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. Science Bulletin. 2019; 65 (3):182-187.
Chicago/Turabian StylePeng Gong; Bin Chen; Xuecao Li; Han Liu; Jie Wang; Yuqi Bai; Jingming Chen; Xi Chen; Lei Fang; Shuailong Feng; Yongjiu Feng; Yali Gong; Hao Gu; Huabing Huang; Xiaochun Huang; Hongzan Jiao; Yingdong Kang; Guangbin Lei; Ainong Li; Xiaoting Li; Xun Li; Yuechen Li; Zhilin Li; Zhongde Li; Chong Liu; Chunxia Liu; Maochou Liu; Shuguang Liu; Wanliu Mao; Changhong Miao; Hao Ni; Qisheng Pan; Shuhua Qi; Zhehao Ren; Zhuoran Shan; Shaoqing Shen; Minjun Shi; Yimeng Song; Mo Su; Hoi Ping Suen; Bo Sun; Fangdi Sun; Jian Sun; Lin Sun; Wenyao Sun; Tian Tian; Xiaohua Tong; Yihsing Tseng; Ying Tu; Hong Wang; Lan Wang; Xi Wang; Zongming Wang; Tinghai Wu; Yaowen Xie; Jian Yang; Jun Yang; Man Yuan; Wenze Yue; Hongda Zeng; Kuo Zhang; Neng Zhang; Tao Zhang; Yu Zhang; Feng Zhao; Yichen Zheng; Qiming Zhou; Nicholas Clinton; Zhiliang Zhu; Bing Xu. 2019. "Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018." Science Bulletin 65, no. 3: 182-187.