Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan

[1]  Huan Liu,et al.  Modeling post-disaster business recovery under partially observed states: A case study of the 2011 great East Japan earthquake , 2022, Journal of Cleaner Production.

[2]  H. Tatano,et al.  Multistate Models for the Recovery Process in the Covid-19 Context: An Empirical Study of Chinese Enterprises , 2022, International Journal of Disaster Risk Science.

[3]  Chaolin Gu,et al.  A community-level study on COVID-19 transmission and policy interventions in Wuhan, China , 2022, Cities.

[4]  Zhe Zhang,et al.  Exploring the spatial disparity of home‐dwelling time patterns in the USA during the COVID‐19 pandemic via Bayesian inference , 2022, Trans. GIS.

[5]  H. Tatano,et al.  Estimating the Economic Effects of the Early Covid-19 Emergency Response in Cities Using Intracity Travel Intensity Data , 2022, International Journal of Disaster Risk Science.

[6]  Hongwei Dong,et al.  Urban Recovery from the COVID-19 Pandemic in Beijing, China , 2022, The Professional Geographer.

[7]  Xiao Huang,et al.  The times, they are a-changin’: tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia , 2022, BMJ Global Health.

[8]  J. Zhang,et al.  Association of human mobility with road crashes for pandemic-ready safer mobility: A New York City case study , 2021, Accident Analysis & Prevention.

[9]  Nallapaneni Manoj Kumar,et al.  Impacts of COVID-19 on Sustainable Development Goals and effective approaches to maneuver them in the post-pandemic environment , 2021, Environmental Science and Pollution Research.

[10]  M. Pompili,et al.  Mental Health in the Time of COVID-19 Pandemic: A Worldwide Perspective , 2021, International journal of environmental research and public health.

[11]  Laura Alessandretti What human mobility data tell us about COVID-19 spread , 2021, Nature reviews. Physics.

[12]  Elisabeth R. Gerber,et al.  The role of human and social capital in earthquake recovery in Nepal , 2021, Nature Sustainability.

[13]  Junyi Zhang,et al.  “What should be computed” for supporting post-pandemic recovery policymaking? A life-oriented perspective , 2021, Computational Urban Science.

[14]  Junhua Zhao,et al.  Electricity-consumption data reveals the economic impact and industry recovery during the pandemic , 2021, Scientific Reports.

[15]  Rubén Fernández Pozo,et al.  Characterization of COVID-19’s Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain , 2021, Sensors.

[16]  Minyoung Jang,et al.  Human Mobility Change Pattern and Influencing Factors during COVID-19, from the Outbreak to the Deceleration Stage: A Study of Seoul Metropolitan City , 2021, The Professional Geographer.

[17]  Ci Song,et al.  How did human dwelling and working intensity change over different stages of COVID-19 in Beijing? , 2021, Sustainable Cities and Society.

[18]  Xinyan Zhu,et al.  Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges , 2021, Int. J. Digit. Earth.

[19]  J. Patz,et al.  Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race , 2021, Proceedings of the National Academy of Sciences.

[20]  Sicheng Wang,et al.  Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the COVID-19 Pandemic , 2021, Annals of the American Association of Geographers.

[21]  A. Tatem,et al.  Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic , 2021, Engineering.

[22]  A. Vespignani,et al.  The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China , 2021, Science Advances.

[23]  Constantine E. Kontokosta,et al.  Measuring inequality in community resilience to natural disasters using large-scale mobility data , 2021, Nature Communications.

[24]  C. Jaeger,et al.  Mechanisms of recurrent outbreak of COVID-19: a model-based study , 2021, Nonlinear Dynamics.

[25]  Pengpeng Xu,et al.  The effect of human mobility and control measures on traffic safety during COVID-19 pandemic , 2021, PloS one.

[26]  J. Shearston,et al.  Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data , 2021, medRxiv.

[27]  Tao Ye,et al.  A new approach to estimating flood-affected populations by combining mobility patterns with multi-source data: A case study of Wuhan, China , 2021 .

[28]  Akimasa Fujiwara,et al.  Analysis of post-disaster population movement by using mobile spatial statistics , 2021 .

[29]  Rajluxmee Beejadhursing,et al.  Government Intervention Measures Effectively Control COVID-19 Epidemic in Wuhan, China , 2021, Current Medical Science.

[30]  M. M. Masud,et al.  Impact of COVID-19 on sustainable development , 2021 .

[31]  Wenting Zou,et al.  A geographical detector study on factors influencing urban park use in Nanjing, China , 2021 .

[32]  A. Pentland,et al.  Effect of COVID-19 response policies on walking behavior in US cities , 2020, Nature Communications.

[33]  Jure Leskovec,et al.  Mobility network models of COVID-19 explain inequities and inform reopening , 2020, Nature.

[34]  Andrew M. Dai,et al.  Impacts of social distancing policies on mobility and COVID-19 case growth in the US , 2020, Nature Communications.

[35]  Xueli Wang,et al.  COVID-19, Urbanization Pattern and Economic Recovery: An Analysis of Hubei, China , 2020, International journal of environmental research and public health.

[36]  Roger C. Shouse,et al.  Disaster resilience through big data: Way to environmental sustainability , 2020 .

[37]  Lei Zhang,et al.  Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States , 2020, Scientific Reports.

[38]  Paolo Beria,et al.  Presence and mobility of the population during the first wave of Covid-19 outbreak and lockdown in Italy , 2020, Sustainable Cities and Society.

[39]  M. Bachmann,et al.  Post-lockdown SARS-CoV-2 nucleic acid screening in nearly ten million residents of Wuhan, China , 2020, Nature Communications.

[40]  Lei Zhang,et al.  Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections , 2020, Proceedings of the National Academy of Sciences.

[41]  Caroline O Buckee,et al.  The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology , 2020, Nature Communications.

[42]  Xiao Huang,et al.  Time-series clustering for home dwell time during COVID-19: what can we learn from it? , 2020, medRxiv.

[43]  D. Larremore,et al.  Reductions in commuting mobility correlate with geographic differences in SARS-CoV-2 prevalence in New York City , 2020, Nature Communications.

[44]  Yuliang Zou,et al.  Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan , 2020, International journal of environmental research and public health.

[45]  R. Heale,et al.  Mental health in the time of COVID-19 , 2020, Evidence Based Journals.

[46]  C. Jaeger,et al.  Lessons from the Mainland of China’s Epidemic Experience in the First Phase about the Growth Rules of Infected and Recovered Cases of COVID-19 Worldwide , 2020, International Journal of Disaster Risk Science.

[47]  Zuxun Lu,et al.  Psychological health, sleep quality, and coping styles to stress facing the COVID-19 in Wuhan, China , 2020, Translational Psychiatry.

[48]  Jinliang Huang,et al.  Data-driven framework for delineating urban population dynamic patterns: Case study on Xiamen Island, China , 2020, Sustainable Cities and Society.

[49]  Murat Sönmez,et al.  Assessment of the Disaster Recovery Progress through Mathematical Modelling , 2020 .

[50]  Building back better: A sustainable, resilient recovery after COVID-19 , 2020, OECD Policy Responses to Coronavirus (COVID-19).

[51]  Yi Qiang,et al.  Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster , 2020 .

[52]  Xia Wu,et al.  A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster , 2020 .

[53]  Carl A. B. Pearson,et al.  Changing travel patterns in China during the early stages of the COVID-19 pandemic , 2020, Nature Communications.

[54]  N. Fujiwara,et al.  Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic , 2020, Scientific Reports.

[55]  Kevin M. Blanchard,et al.  Are we there yet? The transition from response to recovery for the COVID-19 pandemic , 2020, Progress in Disaster Science.

[56]  A. Vespignani,et al.  Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China , 2020, Science.

[57]  Jianmin Jia,et al.  Population flow drives spatio-temporal distribution of COVID-19 in China , 2020, Nature.

[58]  Hitomu Kotani,et al.  Transition of post-disaster housing of rural households: A case study of the 2015 Gorkha earthquake in Nepal , 2020 .

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

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

[61]  F. Dominici,et al.  Aggregated mobility data could help fight COVID-19 , 2020, Science.

[62]  Yoshihide Sekimoto,et al.  Understanding post-disaster population recovery patterns , 2020, Journal of the Royal Society Interface.

[63]  O. Murao Recovery curves for housing reconstruction from the 2011 Great East Japan Earthquake and comparison with other post-disaster recovery processes , 2020 .

[64]  A. Yeh,et al.  Analyzing spatial relationships between urban land use intensity and urban vitality at street block level: A case study of five Chinese megacities , 2020 .

[65]  Lingli Tu,et al.  Research on Population Spatiotemporal Aggregation Characteristics of a Small City: A Case Study on Shehong County Based on Baidu Heat Maps , 2019, Sustainability.

[66]  Li Zhang,et al.  Using multi-source big data to understand the factors affecting urban park use in Wuhan , 2019, Urban Forestry & Urban Greening.

[67]  Jingang Li,et al.  Spatiotemporal distribution characteristics and mechanism analysis of urban population density: A case of Xi'an, Shaanxi, China , 2019, Cities.

[68]  Junwei Wang,et al.  Resilience of Transportation Systems: Concepts and Comprehensive Review , 2019, IEEE Transactions on Intelligent Transportation Systems.

[69]  Wentao Yang,et al.  Decreased post-seismic landslides linked to vegetation recovery after the 2008 Wenchuan earthquake , 2018, Ecological Indicators.

[70]  T. Blaschke,et al.  Measuring the progress of a recovery process after an earthquake: The case of L'aquila, Italy , 2017, International Journal of Disaster Risk Reduction.

[71]  Pierluigi Mancarella,et al.  Metrics and Quantification of Operational and Infrastructure Resilience in Power Systems , 2017, IEEE Transactions on Power Systems.

[72]  Hong Yang,et al.  Data-Driven Spatial Modeling for Quantifying Networkwide Resilience in the Aftermath of Hurricanes Irene and Sandy , 2017 .

[73]  Kash Barker,et al.  A review of definitions and measures of system resilience , 2016, Reliab. Eng. Syst. Saf..

[74]  Elizabeth Ann Maharaj,et al.  Time-Series Clustering , 2015 .

[75]  C. Burton A Validation of Metrics for Community Resilience to Natural Hazards and Disasters Using the Recovery from Hurricane Katrina as a Case Study , 2015 .

[76]  Ying Wang,et al.  The time process of post-earthquake recovery: the Yao'an earthquake in China. , 2014, Disasters.

[77]  Kash Barker,et al.  Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows , 2014, Risk analysis : an official publication of the Society for Risk Analysis.

[78]  Ming Wang,et al.  Diagnosis of Vegetation Recovery in Mountainous Regions After the Wenchuan Earthquake , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[79]  Teresa M Adams,et al.  Freight Resilience Measures , 2012 .

[80]  A. Kaasa,et al.  The Role of Human and Social Capital for Innovation in Catching-Up Economies , 2012 .

[81]  Jerry T. Mitchell,et al.  Evaluating post-Katrina recovery in Mississippi using repeat photography. , 2011, Disasters.

[82]  O. Murao,et al.  RECOVERY CURVES FOR HOUSING RECONSTRUCTION IN SRI LANKA AFTER THE 2004 INDIAN OCEAN TSUNAMI , 2010 .

[83]  J. Stringfield Higher ground: an exploratory analysis of characteristics affecting returning populations after Hurricane Katrina , 2010 .

[84]  J. Pujol The solution of nonlinear inverse problems and the Levenberg-Marquardt method , 2007 .

[85]  Michel Bruneau,et al.  A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities , 2003 .