A new disease mapping method for improving data completeness of syndromic surveillance with high missing rates
文献类型：期刊论文
| 作者 | Liao, Yilan5; Shi, Yuanhao4,5; Fan, Zhirui3; Zhu, Zhiyu2; Huang, Binghu3; Du, Wei1; Wang, Jinfeng5; Wang, Liping6 |
| 刊名 | TRANSACTIONS IN GIS
 |
| 出版日期 | 2024-07-16 |
| 卷号 | N/A |
| DOI | 10.1111/tgis.13200 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Syndromic surveillance is a type of public health surveillance that utilizes nonspecific indicators or symptoms associated with a particular disease or condition to detect and track disease outbreaks early. However, data completeness has been a significant challenge for syndromic surveillance systems in many countries. Incomplete data may make it difficult to accurately identify anomalies or trends in surveillance data. In this study, a new disease mapping method based on a high-accuracy, low-rank tensor completion (HaLRTC) algorithm is proposed to estimate the quarterly positivity rate of the human influenza virus (IFV) based on highly insufficient 2010-2015 respiratory syndromic surveillance data from the subtropical monsoon region of China. The HaLRTC algorithm is a spatiotemporal interpolation method applied to fill in missing or incomplete data using a low-rank tensor structure. The results show that the accuracy (R2 = 0.880, RMSE = 0.037) of the proposed method is much higher than that of three traditional disease mapping methods: Cokriging, hierarchical Bayesian, and sandwich estimation methods. This study provides a new disease mapping approach to improve the quality and completeness of data in syndrome surveillance or other familiar systems with a large proportion of missing data. |
| WOS关键词 | REGRESSION ; EPIDEMIC |
| WOS研究方向 | Geography |
| WOS记录号 | WOS:001268459800001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206050]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Liao, Yilan |
| 作者单位 | 1.Southeast Univ, Sch Publ Hlth, Key Lab Environm Med Engn, Minist Educ, Nanjing, Peoples R China 2.China Mobile Xiongan Ind Res Inst, Baoding, Peoples R China 3.China Univ Petr, Coll Oceanog & Space Informat, Qingdao, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 6.Chinese Ctr Dis Control & Prevent, Div Infect Dis, Key Lab Surveillance & Early Warning Infect Dis, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liao, Yilan,Shi, Yuanhao,Fan, Zhirui,et al. A new disease mapping method for improving data completeness of syndromic surveillance with high missing rates[J]. TRANSACTIONS IN GIS,2024,N/A. |
| APA | Liao, Yilan.,Shi, Yuanhao.,Fan, Zhirui.,Zhu, Zhiyu.,Huang, Binghu.,...&Wang, Liping.(2024).A new disease mapping method for improving data completeness of syndromic surveillance with high missing rates.TRANSACTIONS IN GIS,N/A. |
| MLA | Liao, Yilan,et al."A new disease mapping method for improving data completeness of syndromic surveillance with high missing rates".TRANSACTIONS IN GIS N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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