Comparing the timeliness of three types of influenza surveillance data in mainland China
10.16462/j.cnki.zhjbkz.2019.04.004
- Author:
Kang QIN
1
;
Ye-wu ZHANG
;
Peng ZHANG
;
Yan-fei LI
;
Jia-qi. MA
Author Information
1. Public Health Surveillance and Information Service Center, Chinese Center for Disease Control and Prevention, Beijing 102206, China
- Publication Type:Research Article
- Keywords:
Influenza;
Surveillance data;
Timeliness;
Comparison
- From:
Chinese Journal of Disease Control & Prevention
2019;23(4):387-391
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the timeliness of the three sets of influenza surveillance data (influenza reported cases from Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS), influenza-like illness consultation rate (ILI%) and influenza virus positive rate from Chinese Influenza Surveillance Information System) in mainland China. Methods The three sets of influenza surveillance data of North and South China from 2017 to 2018 were compared using peak comparison, cross correlation and Early Aberration Reporting System C3 method. Results The influenza epidemic trends reflected by the three sets of influenza surveillance weekly data from 2017 to 2018 were generally consistent and significantly correlated. However, the three sets of data had different timeliness. From 2017 to 2018, ILI% in the North was not timely at alarming the first epidemic peak, which was 6 weeks and 9 weeks later than influenza cases from NIDRIS and positive rate of influenza virus respectively. While in the South, ILI% was the most sensitive indicator, which was 4 weeks and 7 weeks earlier than influenza cases from NIDRIS and positive rate of influenza virus respectively. However, the three sets of data had little difference in the timeliness of the second epidemic peak both in the North and South. Conclusions The three sets of influenza surveillance data in mainland China could all roughly reflected the epidemic trend of influenza. After comparing the timeliness, a combination of influenza reported cases from NIDRIS together with ILI% and influenza virus positive rate could improve timeliness and accuracy for early warning of influenza.
