Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan
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Saini Yang | Xiao Huang | Qiangqiang Xiong | Rui An | Xiaoyan Liu | Tao Ye
[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 .