Optimizing the detection of emerging infections using mobility-based spatial sampling
Optimizing the detection of emerging infections using mobility-based spatial sampling
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.
Data analysis, Emerging infectious disease, Human mobility, Spatial sampling, Testing allocation
Zhang, Die
6bd2fa61-6916-4ac4-8514-81d8ef98a62a
Ge, Yong
f22fa40c-9a6a-456c-bdad-b322c3fd24ee
Wang, Jianghao
824eda0f-b65e-41c4-bb75-b0b604f96454
Liu, Haiyan
aeca8fb6-ed13-471e-96ec-a33757a3b2e8
Zhang, Wen-Bin
a4ab325c-e9cb-4369-959b-25a3320bb4e3
Wu, Xilin
58bc70e9-e062-4a74-8b9c-d3212e505436
Heuvelink, Gerard B.M.
958daf54-59ef-4257-b988-4b2f2d6a6caa
Wu, Chaoyang
1a32f8e7-d58f-4354-a2f2-f2368981eafe
Yang, Juan
a7a93a88-e671-435d-8845-9a32087cfe77
Ruktanonchai, Nick W.
fe68cb8d-3760-4955-99fa-47d43f86580a
Qader, Sarchil
e8e721d4-9706-4b5e-94ee-262042a268ed
Ruktanonchai, Corrine W.
44e6fcd0-246b-480e-8940-9557dbb7c0cc
Cleary, Eimear
3cbf7016-269e-4517-ab4f-323e86db6e58
Yao, Yongcheng
3f67de16-3437-4e42-b0e7-3cab4d90f89a
Liu, Jian
03c42a59-6f39-4069-a4b0-464958554e96
Nnanatu, Chibuzor C.
24be7c1b-a677-4086-91b4-a9d9b1efa5a3
Wesolowski, Amy
343b0df8-5a2f-46e2-9f1c-001d4adf7fb1
Cummings, Derek A.T.
9a136236-0c3f-49a9-8348-591a759b3f80
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
July 2024
Zhang, Die
6bd2fa61-6916-4ac4-8514-81d8ef98a62a
Ge, Yong
f22fa40c-9a6a-456c-bdad-b322c3fd24ee
Wang, Jianghao
824eda0f-b65e-41c4-bb75-b0b604f96454
Liu, Haiyan
aeca8fb6-ed13-471e-96ec-a33757a3b2e8
Zhang, Wen-Bin
a4ab325c-e9cb-4369-959b-25a3320bb4e3
Wu, Xilin
58bc70e9-e062-4a74-8b9c-d3212e505436
Heuvelink, Gerard B.M.
958daf54-59ef-4257-b988-4b2f2d6a6caa
Wu, Chaoyang
1a32f8e7-d58f-4354-a2f2-f2368981eafe
Yang, Juan
a7a93a88-e671-435d-8845-9a32087cfe77
Ruktanonchai, Nick W.
fe68cb8d-3760-4955-99fa-47d43f86580a
Qader, Sarchil
e8e721d4-9706-4b5e-94ee-262042a268ed
Ruktanonchai, Corrine W.
44e6fcd0-246b-480e-8940-9557dbb7c0cc
Cleary, Eimear
3cbf7016-269e-4517-ab4f-323e86db6e58
Yao, Yongcheng
3f67de16-3437-4e42-b0e7-3cab4d90f89a
Liu, Jian
03c42a59-6f39-4069-a4b0-464958554e96
Nnanatu, Chibuzor C.
24be7c1b-a677-4086-91b4-a9d9b1efa5a3
Wesolowski, Amy
343b0df8-5a2f-46e2-9f1c-001d4adf7fb1
Cummings, Derek A.T.
9a136236-0c3f-49a9-8348-591a759b3f80
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Zhang, Die, Ge, Yong, Wang, Jianghao, Liu, Haiyan, Zhang, Wen-Bin, Wu, Xilin, Heuvelink, Gerard B.M., Wu, Chaoyang, Yang, Juan, Ruktanonchai, Nick W., Qader, Sarchil, Ruktanonchai, Corrine W., Cleary, Eimear, Yao, Yongcheng, Liu, Jian, Nnanatu, Chibuzor C., Wesolowski, Amy, Cummings, Derek A.T., Tatem, Andrew J. and Lai, Shengjie
(2024)
Optimizing the detection of emerging infections using mobility-based spatial sampling.
International Journal of Applied Earth Observation and Geoinformation, 131, [103949].
(doi:10.1016/j.jag.2024.103949).
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.
Text
v1_covered_9653993b-d2da-4ed6-9b50-4cc94877463c
- Author's Original
Text
1-s2.0-S1569843224003030-main
- Version of Record
More information
Submitted date: 10 November 2023
Accepted/In Press date: 28 May 2024
e-pub ahead of print date: 1 June 2024
Published date: July 2024
Additional Information:
Publisher Copyright:
© 2024 The Authors
Keywords:
Data analysis, Emerging infectious disease, Human mobility, Spatial sampling, Testing allocation
Identifiers
Local EPrints ID: 485770
URI: http://eprints.soton.ac.uk/id/eprint/485770
ISSN: 0303-2434
PURE UUID: f78193d3-ca26-40e6-a89d-c3d7b991b53b
Catalogue record
Date deposited: 18 Dec 2023 20:38
Last modified: 13 Jul 2024 02:10
Export record
Altmetrics
Contributors
Author:
Die Zhang
Author:
Yong Ge
Author:
Jianghao Wang
Author:
Haiyan Liu
Author:
Wen-Bin Zhang
Author:
Xilin Wu
Author:
Gerard B.M. Heuvelink
Author:
Chaoyang Wu
Author:
Juan Yang
Author:
Nick W. Ruktanonchai
Author:
Sarchil Qader
Author:
Corrine W. Ruktanonchai
Author:
Eimear Cleary
Author:
Yongcheng Yao
Author:
Jian Liu
Author:
Chibuzor C. Nnanatu
Author:
Amy Wesolowski
Author:
Derek A.T. Cummings
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics