A standardised differential privacy framework for epidemiological modeling with mobile phone data
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S. Vadhan | C. Buckee | S. Balsari | N. Kishore | W. Zhang | Navin Vembar | M. Savi | A. Yadav | A. Schroeder | Akash Yadav | N. Vembar | Salil P. Vadhan | Merveille Koissi Savi | Wanrong Zhang | Andrew Schroeder
[1] F. Houssiau,et al. On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data , 2022, Nature communications.
[2] Marta C. González,et al. Mobile phone location data for disasters: A review from natural hazards and epidemics , 2021, Comput. Environ. Urban Syst..
[3] M. Hayward,et al. Interstates of Infection: Preliminary Investigations of Human Mobility Patterns in the COVID‐19 Pandemic , 2021, The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association.
[4] Jure Leskovec,et al. Mobility network models of COVID-19 explain inequities and inform reopening , 2020, Nature.
[5] C. Faes,et al. A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies , 2020, BMC Infectious Diseases.
[6] Caroline O Buckee,et al. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology , 2020, Nature Communications.
[7] Navin Vembar,et al. Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis , 2020, The Lancet Digital Health.
[8] Qingquan Li,et al. Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data , 2020, The Lancet Digital Health.
[9] D Calvetti,et al. Metapopulation Network Models for Understanding, Predicting, and Managing the Coronavirus Disease COVID-19 , 2020, Frontiers in Physics.
[10] Marco De Nadai,et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle , 2020, Science Advances.
[11] Nuno R. Faria,et al. The effect of human mobility and control measures on the COVID-19 epidemic in China , 2020, Science.
[12] Ruiyun Li,et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2) , 2020, Science.
[13] Henry A. Kautz,et al. Hierarchical organization of urban mobility and its connection with city livability , 2019, Nature Communications.
[14] Xinyi Niu,et al. Influence of Built Environment on Urban Vitality: Case Study of Shanghai Using Mobile Phone Location Data , 2019, Journal of Urban Planning and Development.
[15] Luc Rocher,et al. Estimating the success of re-identifications in incomplete datasets using generative models , 2019, Nature Communications.
[16] Oliva G. Cantu-Ros,et al. Influence of sociodemographic characteristics on human mobility , 2018 .
[17] Alessandro D'Alconzo,et al. Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the "Always Connected Era" , 2017, Big-DAMA@SIGCOMM.
[18] Kenth Engø-Monsen,et al. Impact of human mobility on the emergence of dengue epidemics in Pakistan , 2015, Proceedings of the National Academy of Sciences.
[19] A. Tatem,et al. Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data , 2015, Proceedings of the National Academy of Sciences.
[20] Zbigniew Smoreda,et al. On the Use of Human Mobility Proxies for Modeling Epidemics , 2013, PLoS Comput. Biol..
[21] Stephen E. Fienberg,et al. Differential Privacy for Protecting Multi-dimensional Contingency Table Data: Extensions and Applications , 2012, J. Priv. Confidentiality.
[22] Alessandro Vespignani,et al. influenza A(H1N1): a Monte Carlo likelihood analysis based on , 2009 .
[23] Alessandro Vespignani,et al. Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.
[24] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.