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Optimizing the detection of emerging infections using mobility-based spatial sampling

文献类型：期刊论文

作者Zhang, Die17,18; Ge, Yong15,16,17; Wang, Jianghao15,17; Liu, Haiyan14; Zhang, Wen-Bin13,17; Wu, Xilin15,17; Heuvelink, Gerard B. M.11,12; Wu, Chaoyang10,15; Yang, Juan8,9; Ruktanonchai, Nick W.6,13
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2024-07-01
卷号131页码:103949
关键词Human mobility Data analysis Spatial sampling Testing allocation Emerging infectious disease
DOI10.1016/j.jag.2024.103949
产权排序2
文献子类Article
英文摘要Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals ' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost- effective solution to optimize testing resource deployment for containing emerging infectious diseases.
WOS研究方向Remote Sensing
WOS记录号WOS:001250299400001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/205289]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Ge, Yong; Tatem, Andrew J.; Lai, Shengjie
作者单位1.Univ Southampton, Inst Life Sci, Southampton, England
2.Cummings, Derek A. T.] Univ Florida, Emerging Pathogens Inst, Gainesville, FL USA
3.Cummings, Derek A. T.] Univ Florida, Dept Biol, Gainesville, FL USA
4.Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, MD USA
5.Zhengzhou Normal Univ, Sch Math & Stat, Zhengzhou, Peoples R China
6.Qader, Sarchil H.] Univ Sulaimani, Coll Agr Engn Sci, Nat Resources Dept, Sulaimani 334, Kurdistan Regio, Iraq
7.Ruktanonchai, Nick W.; Ruktanonchai, Corrine W.] Virginia Tech, Populat Hlth Sci, Blacksburg, VA USA
8.Fudan Univ, Shanghai Inst Infect Dis & Biosecur, Shanghai, Peoples R China
9.Fudan Univ, Sch Publ Hlth, Key Lab Publ Hlth Safety, Minist Educ, Shanghai, Peoples R China
10.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Die,Ge, Yong,Wang, Jianghao,et al. Optimizing the detection of emerging infections using mobility-based spatial sampling[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,131:103949.
APA Zhang, Die.,Ge, Yong.,Wang, Jianghao.,Liu, Haiyan.,Zhang, Wen-Bin.,...&Lai, Shengjie.(2024).Optimizing the detection of emerging infections using mobility-based spatial sampling.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,131,103949.
MLA Zhang, Die,et al."Optimizing the detection of emerging infections using mobility-based spatial sampling".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 131(2024):103949.

入库方式： OAI收割

来源：地理科学与资源研究所

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