Precise space-time interventions on intra-urban dengue outbreaks using large-scale mobile phone tracking data

Authors: Ling Yin*, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Liang Mao, Department of Geography, University of Florida, Nan Lin, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Xiaoqing Song, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shujiang Mei, Shenzhen Center for Disease Control and Prevention, Shih-Lung Shaw, Department of Geography, University of Tennessee at Knoxville, Zhixiang Fang, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Qinglan Li, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Topics: Geography and Urban Health, Spatial Analysis & Modeling, Transportation Geography
Keywords: mobile phone data, dengue, spatiotemporal analysis, big data, infectious disease
Session Type: Paper
Day: 4/10/2018
Start / End Time: 2:40 PM / 4:20 PM
Room: Lafayette, Marriott, River Tower Elevators, 41st Floor

Large-scale mobile phone tracking data make it possible to depict movements and contacts of individuals in unprecedented details, which indicates a great potential to suggest precise interventions in “space-time-population” dimensions. However, there have been few attempts to explore precise space-time interventions for disease outbreaks using the mobile phone big data.Taking Dengue fever as an example, this study chooses a city-scale to develop a precise space-time intervention framework aided by mobile phone tracking data. We first propose a method to identify target intervention areas at a fine spatial scale and target intervention population at an individual level. Then we design and simulate a series of personalized travel intervention policies from the perspective of changing the spatial dimension alone, temporal dimension alone, and spatiotemporal dimensions together of the targeting individuals’ trajectories. The simulation results show that the proposed intervention policies will generate stable performance due to the regularity of urban residents’ daily travels and activities. This study reveals that each proposed space-time intervention policy has its own advantages and limitations. An appropriate policy-choice depends on multiple factors including the local effect for the targeting areas and the global effect for the entire city, the positive and negative effect of reducing infection risk, the cost of impacting daily life, and the epidemic situation. In summary, this study proposes a new framework demonstrating how to take advantage of the high spatiotemporal resolution of human activity-travel patterns derived from massive mobile phone tracking data to design, simulate, and evaluate space-time intervention policies in urban areas.

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