Academic Journal
Optimizing the detection of emerging infections using mobility-based spatial sampling
| Title: | 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) |
| Publisher Information: | Elsevier, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Physical geography LCC:Environmental sciences |
| Subject Terms: | Human mobility, Data analysis, Spatial sampling, Testing allocation, Emerging infectious disease, Physical geography, GB3-5030, Environmental sciences, GE1-350 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1569-8432 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S1569843224003030; https://doaj.org/toc/1569-8432 |
| DOI: | 10.1016/j.jag.2024.103949 |
| Access URL: | https://doaj.org/article/6d33caa6925e4831ad26ced7493634d0 |
| Accession Number: | edsdoj.6d33caa6925e4831ad26ced7493634d0 |
| Database: | Directory of Open Access Journals |
| ISSN: | 15698432 |
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| DOI: | 10.1016/j.jag.2024.103949 |
| Published in: | International Journal of Applied Earth Observations and Geoinformation |
| Language: | English |