Japanese
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] 2014-Apr

[Comparing the performance of temporal model and temporal-spatial model for outbreak detection in China Infectious Diseases Automated-alert and Response System, 2011-2013, China].

登録ユーザーのみが記事を翻訳できます
ログインサインアップ
リンクがクリップボードに保存されます
Shengjie Lai
Yilan Liao
Honglong Zhang
Xiaozhou Li
Xiang Ren
Fu Li
Jianxing Yu
Liping Wang
Hongjie Yu
Yajia Lan

キーワード

概要

OBJECTIVE

For providing evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) by comparing the early-warning performance of the temporal model and temporal-spatial model in CIDARS.

METHODS

The application performance for outbreak detection of temporal model and temporal-spatial model simultaneously running among 208 pilot counties in 20 provinces from 2011 to 2013 was compared; the 16 infectious diseases were divided into two classes according to the disease incidence level; cases data in nationwide Notifiable Infectious Diseases Reporting Information System was combined with outbreaks reported to Public Health Emergency Reporting System, by adopting the index of the number of signals, sensitivity, false alarm rate and time for detection.

RESULTS

The overall sensitivity of temporal model and temporal-spatial model for 16 diseases was 96.23% (153/159) and 90.57% (144/159) respectively, without significant difference (Z = -1.604, P = 0.109), and the false alarm rate of temporal model (1.57%, 57 068/3 643 279) was significantly higher than that of temporal-spatial model (0.64%, 23 341/3 643 279) (Z = -3.408, P = 0.001), while the median time for detection of these two models was not significantly different, which was 3.0 days and 1.0 day respectively (Z = -1.334, P = 0.182).For 6 diseases of type I which represent the lower incidence, including epidemic hemorrhagic fever,Japanese encephalitis, dengue, meningococcal meningitis, typhus, leptospirosis, the sensitivity was 100% for both models (8/8, 8/8), and the false alarm rate of both temporal model and temporal-spatial model was 0.07% (954/1 367 437, 900/1 367 437), with the median time for detection being 2.5 days and 3.0 days respectively. The number of signals generated by temporal-spatial model was reduced by 2.29% compared with that of temporal model.For 10 diseases of type II which represent the higher incidence, including mumps, dysentery, scarlet fever, influenza, rubella, hepatitis E, acute hemorrhagic conjunctivitis, hepatitis A, typhoid and paratyphoid, and other infectious diarrhea, the sensitivity of temporal model was 96.03% (145/151), and the sensitivity of temporal-spatial model was 90.07% (136/151), the number of signals generated by temporal-spatial model was reduced by 59.36% compared with that of temporal model. Compared to temporal model, temporal-spatial model reduced both the number of signals and the false alarm rate of all the type II diseases;and the median of outbreak detection time of temporal model and temporal-spatial model was 3.0 days and 1.0 day, respectively.

CONCLUSIONS

Overall, the temporal-spatial model had better outbreak detection performance, but the performance of two different models varies for infectious diseases with different incidence levels, and the adjustment and optimization of the temporal model and temporal-spatial model should be conducted according to specific infectious disease in CIDARS.

Facebookページに参加する

科学に裏打ちされた最も完全な薬草データベース

  • 55の言語で動作します
  • 科学に裏打ちされたハーブ療法
  • 画像によるハーブの認識
  • インタラクティブGPSマップ-場所にハーブをタグ付け(近日公開)
  • 検索に関連する科学出版物を読む
  • それらの効果によって薬草を検索する
  • あなたの興味を整理し、ニュース研究、臨床試験、特許について最新情報を入手してください

症状や病気を入力し、役立つ可能性のあるハーブについて読み、ハーブを入力して、それが使用されている病気や症状を確認します。
*すべての情報は公開された科学的研究に基づいています

Google Play badgeApp Store badge