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

Authors :
Die Zhang
Yong Ge
Jianghao Wang
Haiyan Liu
Wen-Bin Zhang
Xilin Wu
Gerard B. M. Heuvelink
Chaoyang Wu
Juan Yang
Nick W. Ruktanonchai
Sarchil H. Qader
Corrine W. Ruktanonchai
Eimear Cleary
Yongcheng Yao
Jian Liu
Chibuzor C. Nnanatu
Amy Wesolowski
Derek A.T. Cummings
Andrew J. Tatem
Shengjie Lai
Source :
International Journal of Applied Earth Observations and Geoinformation, Vol 131, Iss , Pp 103949- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

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.

Details

Language :
English
ISSN :
15698432
Volume :
131
Issue :
103949-
Database :
Directory of Open Access Journals
Journal :
International Journal of Applied Earth Observations and Geoinformation
Publication Type :
Academic Journal
Accession number :
edsdoj.6d33caa6925e4831ad26ced7493634d0
Document Type :
article
Full Text :
https://doi.org/10.1016/j.jag.2024.103949