Dataset Information

Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China.

ABSTRACT: School-based influenza-like-illness (ILI) syndromic surveillance can be an important part of influenza community surveillance by providing early warnings for outbreaks and leading to a fast response. From September 2012 to December 2014, syndromic surveillance of ILI was carried out in 4 county-level schools. The cumulative sum methods(CUSUM) was used to detect abnormal signals. A susceptible-exposed-infectious/asymptomatic-recovered (SEIAR) model was fit to the influenza outbreak without control measures and compared with the actual influenza outbreak to evaluate the effectiveness of early control efforts. The ILI incidence rates in 2014 (14.51%) was higher than the incidence in 2013 (5.27%) and 2012 (3.59%). Ten school influenza outbreaks were detected by CUSUM. Each outbreak had high transmissibility with a median Runc of 4.62. The interventions in each outbreak had high effectiveness and all Rcon were 0. The early intervention had high effectiveness within the school-based ILI syndromic surveillance. Syndromic surveillance within schools can play an important role in controlling influenza outbreaks.

SUBMITTER: Xu W

PROVIDER: S-EPMC5590954 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China.

Xu Wenti W Chen Tianmu T Dong Xiaochun X Kong Mei M Lv Xiuzhi X Li Lin L

PloS one 20170908 9

School-based influenza-like-illness (ILI) syndromic surveillance can be an important part of influenza community surveillance by providing early warnings for outbreaks and leading to a fast response. From September 2012 to December 2014, syndromic surveillance of ILI was carried out in 4 county-level schools. The cumulative sum methods(CUSUM) was used to detect abnormal signals. A susceptible-exposed-infectious/asymptomatic-recovered (SEIAR) model was fit to the influenza outbreak without contro ...[more]

PMID: 28886143

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Project description:School children may transmit pathogens with cluster cases occurring on campuses and in families. In response to the 2009 influenza A (H1N1) pandemic, Taipei City Government officials developed a School-based Infectious Disease Syndromic Surveillance System (SID-SSS). Teachers and nurses from preschools to universities in all 12 districts within Taipei are required to daily report cases of symptomatic children or sick leave requests through the SID-SSS. The pre-diagnosis at schools is submitted firstly as common pediatric disease syndrome-groups and re-submitted after confirmation by physicians. We retrieved these data from January 2010 to August 2011 for spatio-temporal analysis and evaluated the temporal trends with cases obtained from both the Emergency Department-based Syndromic Surveillance System (ED-SSS) and the Longitudinal Health Insurance Database 2005 (LHID2005). Through the SID-SSS, enterovirus-like illness (EVI) and influenza-like illness (ILI) were the two most reported syndrome groups (77.6% and 15.8% among a total of 19,334 cases, respectively). The pre-diagnosis judgments made by school teachers and nurses showed high consistency with physicians' clinical diagnoses for EVI (97.8%) and ILI (98.9%). Most importantly, the SID-SSS had better timeliness with earlier peaks of EVI and ILI than those in the ED-SSS. Furthermore, both of the syndrome groups in these two surveillance systems had the best correlation reaching 0.98 and 0.95, respectively (p<0.01). Spatio-temporal analysis observed the patterns of EVI and ILI both diffuse from the northern suburban districts to central Taipei, with ILI spreading faster. This novel system can identify early suspected cases of two important pediatric infections occurring at schools, and clusters from schools/families. It was also cost-effective (95.5% of the operation cost reduced and 59.7% processing time saved). The timely surveillance of mild EVI and ILI cases integrated with spatial analysis may help public health decision-makers with where to target for enhancing surveillance and prevention measures to minimize severe cases.

Project description:BackgroundInfluenza causes considerable disease burden each year, particularly in children. Monitoring school absenteeism has long been proposed as a surveillance tool of influenza activity in the community, but the practice of school absenteeism could be varying, and the potential of such usage remains unclear.ObjectiveThe aim of this paper is to determine the potential of monitoring school absenteeism as a surveillance tool of influenza.MethodsWe conducted a systematic review of the published literature on the relationship between school absenteeism and influenza activity in the community. We categorized the types of school absenteeism and influenza activity in the community to determine the correlation between these data streams. We also extracted this correlation with different lags in community surveillance to determine the potential of using school absenteeism as a leading indicator of influenza activity.ResultsAmong the 35 identified studies, 22 (63%), 12 (34%), and 8 (23%) studies monitored all-cause, illness-specific, and influenza-like illness (ILI)-specific absents, respectively, and 16 (46%) used quantitative approaches and provided 33 estimates on the temporal correlation between school absenteeism and influenza activity in the community. The pooled estimate of correlation between school absenteeism and community surveillance without lag, with 1-week lag, and with 2-week lag were 0.44 (95% CI 0.34, 0.53), 0.29 (95% CI 0.15, 0.42), and 0.21 (95% CI 0.11, 0.31), respectively. The correlation between influenza activity in the community and ILI-specific absenteeism was higher than that between influenza activity in community all-cause absenteeism. Among the 19 studies that used qualitative approaches, 15 (79%) concluded that school absenteeism was in concordance with, coincided with, or was associated with community surveillance. Of the 35 identified studies, only 6 (17%) attempted to predict influenza activity in the community from school absenteeism surveillance.ConclusionsThere was a moderate correlation between school absenteeism and influenza activity in the community. The smaller correlation between school absenteeism and community surveillance with lag, compared to without lag, suggested that careful application was required to use school absenteeism as a leading indicator of influenza epidemics. ILI-specific absenteeism could monitor influenza activity more closely, but the required resource or school participation willingness may require careful consideration to weight against the associated costs. Further development is required to use and optimize the use of school absenteeism to predict influenza activity. In particular, the potential of using more advanced statistical models and validation of the predictions should be explored.

Project description:BackgroundRapid reporting of human infections with novel influenza A viruses accelerates detection of viruses with pandemic potential and implementation of an effective public health response. After detection of human infections with influenza A (H3N2) variant (H3N2v) viruses associated with agricultural fairs during August 2016, the Michigan Department of Health and Human Services worked with the US Centers for Disease Control and Prevention (CDC) to identify infections with variant influenza viruses using a text-based illness monitoring system.ObjectiveTo enhance detection of influenza infections using text-based monitoring and evaluate the feasibility and acceptability of the system for use in future outbreaks of novel influenza viruses.MethodsDuring an outbreak of H3N2v virus infections among agricultural fair attendees, we deployed a text-illness monitoring (TIM) system to conduct active illness surveillance among households of youth who exhibited swine at fairs. We selected all fairs with suspected H3N2v virus infections. For fairs without suspected infections, we selected only those fairs that met predefined criteria. Eligible respondents were identified and recruited through email outreach and/or on-site meetings at fairs. During the fairs and for 10 days after selected fairs, enrolled households received daily, automated text-messages inquiring about illness; reports of illness were investigated by local health departments. To understand the feasibility and acceptability of the system, we monitored enrollment and trends in participation and distributed a Web-based survey to households of exhibitors from five fairs.ResultsAmong an estimated 500 households with a member who exhibited swine at one of nine selected fairs, representatives of 87 (17.4%) households were enrolled, representing 392 household members. Among fairs that were ongoing when the TIM system was deployed, the number of respondents peaked at 54 on the third day of the fair and then steadily declined throughout the rest of the monitoring period; 19 out of 87 household representatives (22%) responded through the end of the 10-day monitoring period. We detected 2 H3N2v virus infections using the TIM system, which represents 17% (2/12) of all H3N2v virus infections detected during this outbreak in Michigan. Of the 70 survey respondents, 16 (23%) had participated in the TIM system. A total of 73% (11/15) participated because it was recommended by fair coordinators and 80% (12/15) said they would participate again.ConclusionsUsing a text-message system, we monitored for illness among a large number of individuals and households and detected H3N2v virus infections through active surveillance. Text-based illness monitoring systems are useful for detecting novel influenza virus infections when active monitoring is necessary. Participant retention and testing of persons reporting illness are critical elements for system improvement.

Dataset Information

Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China.

Publications

Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China.

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