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Coronavirus disease 2019 outbreak in Beijing's Xinfadi Market, China: a modeling study to inform future resurgence response

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Infectious Diseases of Poverty 2021年第3期10卷 122-123页

作者： Xiao-Li Wang Xin Lin Peng Yang Zun-You Wu Gang Li Jennifer M.McGoogan Zeng-Tao Jiao Xin-Jun He Si-Qi Li Hong-Hao Shi Jing-Yuan Wang Sheng-Jie Lai Chun Huang Quan-Yi Wang Beijing Research Center for Preventive Medicine Beijing Center for Disease Prevention and ControlBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK 不详

Background:A local coronavirus disease 2019(COVID-19)case confirmed on June 11,2020 triggered an outbreak in Beijing,China after 56 consecutive days without a newly confirmed case.Non-pharmaceutical interventions(NPIs)were used to contain the source in Xinfadi(XFD)market.To rapidly control the outbreak,both traditional and newly introduced NPIs in eluding large-scale management of high-risk populations and expanded severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)PCR-based screening in the general population were conducted in Beijing.We aimed to assess the effectiveness of the response to the COVID-19 outbreak in Beijing's XFD market and inform future response efforts of resurgence across regions.

关键词： Public health Nonpharmaceutical intervention COVID-19 SARS-CoV-2 Beijing

应急状态下新冠流行曲线预测的方法学研究——基于北京市百度搜索和传统流感样病例监测

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Engineering 2023年第12期31卷 112-119页

作者： Ting Zhang Liuyang Yang Xuan Han Guohui Fan Jie Qian Xuancheng Hu Shengjie Lai Zhongjie Li Zhimin Liu Luzhao Feng Weizhong Yang School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Department of management science and information system Faculty of Management and EconomicsKunming University of Science and TechnologyKunming 650504China WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthampton SO171BJUK The Third Affiliated Hospital of Kunming Medical University Kunming 650118China

Surveillance is an essential work on infectious diseases prevention and control.When the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore new surveillance methods.This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness(ILI)surveillance.A novel hybrid model(multiattention bidirectional gated recurrent unit(MABG)-susceptible-exposed-infected-removed(SEIR))was developed,which leveraged a deep learning algorithm(MABG)to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever,pyrexia,cough,sore throat,anti-fever medicine,and runny nose.By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019(COVID-19)cases,a transmission dynamics model(SEIR)was formulated to estimate the transmission dynamics and epidemic curve of SARS-Co V-2.During the COVID-19 pandemic,when conventional surveillance measures have been suspended temporarily,cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19.In the specific case of Beijing,it has been ascertained that cumulative infection attack rate surpass 80.25%(95%confidence interval(95%CI):77.51%-82.99%)since December 17,2022,with the apex of the outbreak projected to transpire on December 12.The culmination of existing patients is expected to occur three days subsequent to this peak.Effective reproduction number(Rt)represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic,remained below 1 since December 17,2022.The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support.Modern surveillance channels should

关键词： COVID-19 Epidemic curve Baidu search engine Influenza-like illness Deep learning Transmission dynamics model

Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic

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Engineering 2021年第7期7卷 914-923页

作者： Shengjie Lai Nick W.Ruktanonchai Alessandra Carioli Corrine W.Ruktanonchai Jessica R.Floyd Olivia Prosper Chi Zhang Xiangjun Du Weizhong Yang Andrew J.Tatem WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthampton SO171BJUK Population Health Sciences Virginia TechBlacksburgVA 24061USA Department of Mathematics University of TennesseeKnoxvilleTN 37996USA School of Public Health(Shenzhen) Sun Yat-sen UniversityShenzhen 510275China School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019(COVID-19)pandemic,but studies are needed to understand their effectiveness across regions and time.Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020,we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions.We found that if these interventions had not been deployed,the cumulative number of cases could have shown a 97-fold(interquartile range 79–116)increase,as of May 31,2020.However,their effectiveness depended upon the timing,duration,and intensity of the interventions,with variations in case severity seen across populations,regions,and seasons.Additionally,before effective vaccines are widely available and herd immunity is achieved,our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.

关键词： COVID-19 Pandemic Population mobility Travel restriction Physical distancing

Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

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Big Earth Data 2019年第2期3卷 108-139页

作者： Christopher T.Lloyd Heather Chamberlain David Kerr Greg Yetman Linda Pistolesi Forrest R.Stevens Andrea E.Gaughan Jeremiah J.Nieves Graeme Hornby Kytt MacManus Parmanand Sinha Maksym Bondarenko Alessandro Sorichetta Andrew J.Tatem WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK Flowminder Foundation StockholmSweden Center for International Earth Science Information Network(CIESIN) Columbia UniversityPalisadesNYUSA Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA GeoData University of SouthamptonSouthamptonUK

Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://***/10.5258/SOTON/WP00650.

关键词： Human population subnational global spatial dataset multi-temporal

Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

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International Journal of Digital Earth 2020年第1期13卷 78-100页

作者： Forrest R.Stevens Andrea E.Gaughan Jeremiah JNieves Adam King Alessandro Sorichetta Catherine Linard Andrew JTatem Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA WorldPop Department of Geography and EnvironmentUniversity of SouthamptonSouthamptonUK Spatial Epidemiology Lab(SpELL) UniversitéLibre de BruxellesBrusselsBelgium Department of Geography Universitéde NamurNamurBelgium Flowminder Foundation StockholmSweden

Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data.However,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census data.Here we tested two methods using remotely sensed,built-area land cover data to disaggregate population data.These included simple,areal weighting and more complex statistical models with other ancillary information.Outcomes were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental characteristics.We found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built areas.For these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive capacity.We discuss these results and implications for future population modeling approaches.

关键词： Land cover built areas remote sensing settlement mapping population modeling

The Incoming Influenza Season—China,the United Kingdom,and the United States,2021–2022

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China CDC weekly 2021年第49期3卷 1039-1045页

作者： Shasha Han Ting Zhang Yan Lyu Shengjie Lai Peixi Dai Jiandong Zheng Weizhong Yang Xiaohua Zhou Luzhao Feng Beijing International Center for Mathematical Research Peking UniversityBeijingChina Harvard Medical School Harvard UniversityBostonMAUSA School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingChina WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK Division for Infectious Diseases Chinese Center for Disease Control and PreventionBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijingChina.

Introduction:Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)transmission.There has been scarce information to understand the future dynamics of influenza—and under different hypothesis on relaxation of nonpharmaceutical interventions(NPIs)in particular—after the disruptions to seasonal patterns.Methods:We collected data from public sources in China,the United Kingdom,and the United States,and forecasted the influenza dynamics in the incoming 2021–2022 season under different NPIs.We considered Northern China and Southern China separately,due to the sharp difference in the patterns of seasonal influenza.For the United Kingdom,data were collected for England only.Results:Compared to the epidemics in 2017–2019,longer and blunter influenza outbreaks could occur should NPIs be fully lifted,with percent positivity varying from 10.5 to 18.6 in the studying regions.The rebounds would be smaller if the maskwearing intervention continued or the international mobility stayed low,but sharper if the mask-wearing intervention was lifted in the middle of influenza season.Further,influenza activity could stay low under a much less stringent mask-wearing intervention coordinated with influenza vaccination.Conclusions:The results added to our understandings of future influenza dynamics after the global decline during the coronavirus disease 2019(COVID-19)pandemic.In light of the uncertainty on the incoming circulation strains and the relatively low negative impacts of mask wearing on society,our findings suggested that wearing mask could be considered as an accompanying mitigation measure in influenza prevention and control,especially for seasons after long periods of low-exposure to influenza viruses.Seasonal influenza activity declines globally during the coronavirus disease 2019(COVID-19)pandemic(1–4).For instance,in China,influenza activity,as measured by percentage of submitted specimens testing positive,dropped from 11.8%to 2.0%in 2020–2021

关键词： prevention globally continued

COVID-19大流行期间流感活动呈“断崖式”下降——佩戴口罩、人员流动变化及SARS-CoV-2干扰的作用

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Engineering 2023年第2期21卷 195-202,M0008页

作者：韩莎莎张婷吕岩赖圣杰戴佩希郑建东杨维中周晓华冯录召 Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Harvard Medical School Harvard UniversityBostonMA 02115USA School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijing 100871China WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthampton SO171BJUK Division for Infectious Diseases Chinese Center for Disease Control and PreventionBeijing 102206China Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijing 100871China National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijing 100871China

一般情况下,每年冬季是季节性流感高发季节,但在当前2019冠状病毒病(COVID-19)大流行期间,全球季节性流感活动呈“断崖式”下降。为应对即将到来的流感季节,亟需弄清这种前所未有的流感低水平流行的原因。本文中,我们探索了一种国家特... 详细信息

一般情况下,每年冬季是季节性流感高发季节,但在当前2019冠状病毒病(COVID-19)大流行期间,全球季节性流感活动呈“断崖式”下降。为应对即将到来的流感季节,亟需弄清这种前所未有的流感低水平流行的原因。本文中,我们探索了一种国家特异性推理模型,利用该模型估计中国、英国和美国这三个国家中佩戴口罩、人员流动变化(国际和国内)以及严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)干扰的影响。结果发现,在这些地区增加一周佩戴口罩的干预措施,流感活动可减少11.3%~35.2%。实施一周的限制人员流动措施对国际(1.7%~6.5%)和国内社区(1.6%~2.8%)的影响较小。2020年至2021年,仅佩戴口罩这一项干预措施就可使阳性率下降13.3%至19.8%。仅人员流动变化可使阳性率降低5.2%至14.0%,其中79.8%~98.2%归因于国际旅行限制。SARS-CoV-2干扰仅在2019年至2020年具有统计学显著效应。中国北方地区及英国的阳性百分比分别降低7.6%(2.4~14.4)和10.2%(7.2~13.6)。探索所得出的结果对了解非药物干预措施和其他呼吸系统疾病背景下的流感演变有一定意义,有助于制定卫生政策,并可为公共卫生措施的个性化设计提供信息。

关键词：卫生政策非药物干预呼吸系统疾病流行期间季节性流感流动变化个性化设计模型估计

Mobility in China, 2020: a tale of four phases

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National Science Review 2021年第11期8卷 110-120页

作者： Suoyi Tan Shengjie Lai Fan Fang Ziqiang Cao Bin Sai Bing Song Bitao Dai Shuhui Guo Chuchu Liu Mengsi Cai Tong Wang Mengning Wang Jiaxu Li Saran Chen Shuo Qin Jessica R.Floyd Zhidong Cao Jing Tan Xin Sun Tao Zhou Wei Zhang Andrew J.Tatem Petter Holme Xiaohong Chen Xin Lu School of Business Central South University Institute of Big Data and Internet Innovations Hunan University of Technology and Business College of Systems Engineering National University of Defense Technology WorldPop School of Geography and Environmental ScienceUniversity of Southampton School of Mathematics and Big Data Foshan University State Key Laboratory on Blind Signal Processing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Chinese Evidence-Based Medicine Center National Clinical Research Center for Geriatrics West China Hospital Sichuan University Big Data Research Center University of Electronic Science and Technology of China West China Biomedical Big Data Center West China Hospital Sichuan University Tokyo Tech World Hub Research Initiative Institute of Innovative Research Tokyo Institute of Technology

2020 was an unprecedented year,with rapid and drastic changes in human mobility due to the COVID-19 pandemic.To understand the variation in commuting patterns among the Chinese population across stable and unstable periods,we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020,ranging from the Lunar New Year travel season(chunyun), to the exceptional calm of COVID-19 lockdown,and then to the recovery period.We observed that cross-city movements,which increased substantially in chunyun and then dropped sharply during the lockdown,are primarily dependent on travel distance and the socio-economic development of cities.Following the Lunar New Year holiday,national mobility remained low until mid-February,and COVID-19 interventions delayed more than 72.89 million people returning to large cities.Mobility network analysis revealed clusters of highly connected cities,conforming to the social-economic division of urban agglomerations in China.While the mass migration back to large cities was delayed,smaller cities connected more densely to form new clusters.During the recovery period after travel restrictions were lifted,the netflows of over 55% city pairs reversed in direction compared to before the lockdown.These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response,transportation planning and regional economic development,among others.

关键词： human mobility travel restrictions COVID-19 mobile phone data behavioral response

Modelling changing population distributions:an example of the Kenyan Coast,1979–2009

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International Journal of Digital Earth 2017年第10期10卷 1017-1029页

作者： Catherine Linard Caroline W.Kabaria Marius Gilbert Andrew J.Tatem Andrea E.Gaughan Forrest R.Stevens Alessandro Sorichetta Abdisalan M.Noor Robert W.Snow Spatial Epidemiology Lab(SpELL) UniversitéLibre de BruxellesBrusselsBelgium Department of Geography Universitéde NamurNamurBelgium Spatial Health Metrics Group KEMRI Wellcome Trust Research ProgrammeNairobiKenya Fonds National de la Recherche Scientifique(F.R.S.-FNRS) BrusselsBelgium WorldPop Department of Geography and EnvironmentUniversity of SouthamptonSouthamptonUK Fogarty International Center National Institutes of HealthBethesdaMDUSA Flowminder Foundation StockholmSweden Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA Nuffield Department of Clinical Medicine Centre for Tropical Medicine and Global HealthUniversity of OxfordOxfordUK

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates.Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes.Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit.Here we make use of recently released multi-temporal high-resolution global settlement layers,historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast.We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach.Strategies used to fill data gaps may vary according to the local context and the objective of the study.This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.

关键词： Human population distribution modelling gridded population datasets temporal change Kenya