Dataset Information

Early detection of an epidemic erythromelalgia outbreak using Baidu search data.

ABSTRACT: Dozens of epidemic erythromelalgia (EM) outbreaks have been reported in China since the mid-twentieth century, and the most recent happened in Foshan City, Guangdong Province early 2014. This study compared the daily case counts of this recent epidemic EM outbreak from February 11 to March 3 with Baidu search data for the same period. After keyword selection, filtering and composition, the most correlated lag of the EM Search Index was used for comparison and linear regression model development. This study also explored the spatial distribution of epidemic EM in China during this period based on EM Search Index. The EM Search Index at lag 2 was most significantly associated with daily case counts in Foshan (??=?0.863, P?

SUBMITTER: Gu Y

PROVIDER: S-EPMC4517510 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Early detection of an epidemic erythromelalgia outbreak using Baidu search data.

Gu Yuzhou Y Chen Fengling F Liu Tao T Lv Xiaojuan X Shao Zhaoming Z Lin Hualiang H Liang Chaobin C Zeng Weilin W Xiao Jianpeng J Zhang Yonghui Y Huang Cunrui C Rutherford Shannon S Ma Wenjun W

Scientific reports 20150728

Dozens of epidemic erythromelalgia (EM) outbreaks have been reported in China since the mid-twentieth century, and the most recent happened in Foshan City, Guangdong Province early 2014. This study compared the daily case counts of this recent epidemic EM outbreak from February 11 to March 3 with Baidu search data for the same period. After keyword selection, filtering and composition, the most correlated lag of the EM Search Index was used for comparison and linear regression model development. ...[more]

PMID: 26218589

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Project description:BackgroundOutbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms.MethodsExponentially weighted moving average (EWMA), cumulative sum (CUSUM) and moving percentile method (MPM) algorithms were applied. We inserted simulated outbreaks into notifiable infectious disease data in China Infectious Disease Automated-alert and Response System (CIDARS), and compared the performance of the three algorithms with optimized parameters at a fixed false alarm rate of 5% classified by epidemic features of infectious disease. Multiple linear regression was adopted to analyse the relationship of the algorithms' sensitivity and timeliness with the epidemic features of infectious diseases.ResultsThe MPM had better detection performance than EWMA and CUSUM through all simulated outbreaks, with or without stratification by epidemic features (incubation period, baseline counts and outbreak magnitude). The epidemic features were associated with both sensitivity and timeliness. Compared with long incubation, short incubation had lower probability (?*?=?-0.13, P?<?0.001) but needed shorter time to detect outbreaks (?*?=?-0.57, P?<?0.001). Lower baseline counts were associated with higher probability (?*?=?-0.20, P?<?0.001) and longer time (?*?=?0.14, P?<?0.001). The larger outbreak magnitude was correlated with higher probability (?*?=?0.55, P?<?0.001) and shorter time (?*?=?-0.23, P?<?0.001).ConclusionsThe results of this study suggest that the MPM is a prior algorithm for outbreak detection and differences of epidemic features in detection performance should be considered in automatic surveillance practice.

Dataset Information

Early detection of an epidemic erythromelalgia outbreak using Baidu search data.

Publications

Early detection of an epidemic erythromelalgia outbreak using Baidu search data.

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