Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence
Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence
We evaluated a novel strategy to improve the performance of outbreak detection algorithms, namely setting the alerting threshold separately in each region according to the disease incidence in that region. By using data on hand, foot and mouth disease in Shandong province, China, we evaluated the impact of disease incidence on the performance of outbreak detection algorithms (EARS-C1, C2 and C3). Compared to applying the same algorithm and threshold to the whole region, setting the optimal threshold in each region according to the level of disease incidence (i.e., high, middle, and low) enhanced sensitivity (C1: from 94.4% to 99.1%, C2: from 93.5% to 95.4%, C3: from 91.7% to 95.4%) and reduced the number of alert signals (the percentage of reduction is C1?4.3%, C2?11.9%, C3?10.3%). Our findings illustrate a general method for improving the accuracy of detection algorithms that is potentially applicable broadly to other diseases and regions.
e71803
Zhang, Honglong
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Lai, Shengjie
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Wang, Liping
ef5828b8-d874-42db-bb25-713890281af2
Zhao, Dan
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Zhou, Dinglun
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Lan, Yajia
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Buckeridge, David L
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Li, Zhongjie
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Yang, Weizhong
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19 August 2013
Zhang, Honglong
35d0ccf0-0422-4fcd-a949-a74a83f75e51
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Wang, Liping
ef5828b8-d874-42db-bb25-713890281af2
Zhao, Dan
f1f34d2c-5926-4149-94be-c1c09bba11e4
Zhou, Dinglun
4dcccc41-a503-4e9b-aca4-7c06f8ae3bf8
Lan, Yajia
1c3b4eec-04e2-4852-898d-af5ab6a95b07
Buckeridge, David L
701790ad-59c2-477e-94f3-fb2f4ff7bd89
Li, Zhongjie
f89a98f7-f6d3-4312-995a-bc658ae9a93f
Yang, Weizhong
65d18fbc-d752-42a7-ac38-01534ceda15c
Zhang, Honglong, Lai, Shengjie, Wang, Liping, Zhao, Dan, Zhou, Dinglun, Lan, Yajia, Buckeridge, David L, Li, Zhongjie and Yang, Weizhong
(2013)
Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence.
PLoS ONE, 8 (8), .
(doi:10.1371/journal.pone.0071803).
Abstract
We evaluated a novel strategy to improve the performance of outbreak detection algorithms, namely setting the alerting threshold separately in each region according to the disease incidence in that region. By using data on hand, foot and mouth disease in Shandong province, China, we evaluated the impact of disease incidence on the performance of outbreak detection algorithms (EARS-C1, C2 and C3). Compared to applying the same algorithm and threshold to the whole region, setting the optimal threshold in each region according to the level of disease incidence (i.e., high, middle, and low) enhanced sensitivity (C1: from 94.4% to 99.1%, C2: from 93.5% to 95.4%, C3: from 91.7% to 95.4%) and reduced the number of alert signals (the percentage of reduction is C1?4.3%, C2?11.9%, C3?10.3%). Our findings illustrate a general method for improving the accuracy of detection algorithms that is potentially applicable broadly to other diseases and regions.
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Accepted/In Press date: 2 July 2013
Published date: 19 August 2013
Organisations:
WorldPop, Population, Health & Wellbeing (PHeW)
Identifiers
Local EPrints ID: 373598
URI: http://eprints.soton.ac.uk/id/eprint/373598
ISSN: 1932-6203
PURE UUID: ee167f70-e7f4-45f2-9b51-686dbd30b71c
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Date deposited: 22 Jan 2015 16:51
Last modified: 15 Mar 2024 04:02
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Contributors
Author:
Honglong Zhang
Author:
Liping Wang
Author:
Dan Zhao
Author:
Dinglun Zhou
Author:
Yajia Lan
Author:
David L Buckeridge
Author:
Zhongjie Li
Author:
Weizhong Yang
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