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Optimizing the detection of emerging infections using mobility-based spatial sampling
Background Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680910/ https://www.ncbi.nlm.nih.gov/pubmed/38014322 http://dx.doi.org/10.21203/rs.3.rs-3597070/v1 |
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author | Zhang, Die Ge, Yong Wang, Jianghao Liu, Haiyan Zhang, Wen-Bin Wu, Xilin Heuvelink, Gerard Wu, Chaoyang Yang, Juan Ruktanonchai, Nick Qader, Sarchil Ruktanonchai, Corrine Cleary, Eimear Yao, Yongcheng Liu, Jian Nnanatu, Chibuzor Wesolowski, Amy Cummings, Derek Tatem, Andrew Lai, Shengjie |
author_facet | Zhang, Die Ge, Yong Wang, Jianghao Liu, Haiyan Zhang, Wen-Bin Wu, Xilin Heuvelink, Gerard Wu, Chaoyang Yang, Juan Ruktanonchai, Nick Qader, Sarchil Ruktanonchai, Corrine Cleary, Eimear Yao, Yongcheng Liu, Jian Nnanatu, Chibuzor Wesolowski, Amy Cummings, Derek Tatem, Andrew Lai, Shengjie |
author_sort | Zhang, Die |
collection | PubMed |
description | Background Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings. |
format | Online Article Text |
id | pubmed-10680910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-106809102023-11-27 Optimizing the detection of emerging infections using mobility-based spatial sampling Zhang, Die Ge, Yong Wang, Jianghao Liu, Haiyan Zhang, Wen-Bin Wu, Xilin Heuvelink, Gerard Wu, Chaoyang Yang, Juan Ruktanonchai, Nick Qader, Sarchil Ruktanonchai, Corrine Cleary, Eimear Yao, Yongcheng Liu, Jian Nnanatu, Chibuzor Wesolowski, Amy Cummings, Derek Tatem, Andrew Lai, Shengjie Res Sq Article Background Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings. American Journal Experts 2023-11-17 /pmc/articles/PMC10680910/ /pubmed/38014322 http://dx.doi.org/10.21203/rs.3.rs-3597070/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Zhang, Die Ge, Yong Wang, Jianghao Liu, Haiyan Zhang, Wen-Bin Wu, Xilin Heuvelink, Gerard Wu, Chaoyang Yang, Juan Ruktanonchai, Nick Qader, Sarchil Ruktanonchai, Corrine Cleary, Eimear Yao, Yongcheng Liu, Jian Nnanatu, Chibuzor Wesolowski, Amy Cummings, Derek Tatem, Andrew Lai, Shengjie Optimizing the detection of emerging infections using mobility-based spatial sampling |
title |
Optimizing the detection of emerging infections using mobility-based spatial sampling
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title_full |
Optimizing the detection of emerging infections using mobility-based spatial sampling
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title_fullStr |
Optimizing the detection of emerging infections using mobility-based spatial sampling
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title_full_unstemmed |
Optimizing the detection of emerging infections using mobility-based spatial sampling
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title_short |
Optimizing the detection of emerging infections using mobility-based spatial sampling
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title_sort | optimizing the detection of emerging infections using mobility-based spatial sampling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680910/ https://www.ncbi.nlm.nih.gov/pubmed/38014322 http://dx.doi.org/10.21203/rs.3.rs-3597070/v1 |
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