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 im...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3747136?pdf=render |
| _version_ | 1818510615283499008 |
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| author | Honglong Zhang Shengjie Lai Liping Wang Dan Zhao Dinglun Zhou Yajia Lan David L Buckeridge Zhongjie Li Weizhong Yang |
| author_facet | Honglong Zhang Shengjie Lai Liping Wang Dan Zhao Dinglun Zhou Yajia Lan David L Buckeridge Zhongjie Li Weizhong Yang |
| author_sort | Honglong Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T23:22:24Z |
| format | Article |
| id | doaj.art-aac96fd20a57444db4837fdd8e1b7819 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T23:22:24Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-aac96fd20a57444db4837fdd8e1b78192022-12-22T01:29:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0188e7180310.1371/journal.pone.0071803Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence.Honglong ZhangShengjie LaiLiping WangDan ZhaoDinglun ZhouYajia LanDavid L BuckeridgeZhongjie LiWeizhong YangWe 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.http://europepmc.org/articles/PMC3747136?pdf=render |
| spellingShingle | Honglong Zhang Shengjie Lai Liping Wang Dan Zhao Dinglun Zhou Yajia Lan David L Buckeridge Zhongjie Li Weizhong Yang Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence. PLoS ONE |
| title | Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence. |
| title_full | Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence. |
| title_fullStr | Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence. |
| title_full_unstemmed | Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence. |
| title_short | Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence. |
| title_sort | improving the performance of outbreak detection algorithms by classifying the levels of disease incidence |
| url | http://europepmc.org/articles/PMC3747136?pdf=render |
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