title: Estimating the effectiveness of early control measures through school absenteeism surveillance in observed outbreaks at rural schools in Hubei, China.
abstract: BACKGROUND
  School absenteeism is a common data source in syndromic surveillance, which allows for the detection of outbreaks at an early stage. Previous studies focused on its correlation with other data sources. In this study, we evaluated the effectiveness of control measures based on early warning signals from school absenteeism surveillance in rural Chinese schools.
  
  
  METHODS
  A school absenteeism surveillance system was established in all 17 primary schools in 3 adjacent towns in the Chinese region of Hubei. Three outbreaks (varicella, mumps, and influenza-like illness) were detected and controlled successfully from April 1, 2012, to January 15, 2014. An impulse susceptible-exposed-infectious-recovered model was used to fit the epidemics of these three outbreaks. Moreover, it simulated the potential epidemics under interventions resulting from traditional surveillance signals. The effectiveness of the absenteeism-based control measures was evaluated by comparing the simulated datasets.
  
  
  RESULTS
  The school absenteeism system generated 52 signals. Three outbreaks were verified through epidemiological investigation. Compared to traditional surveillance, the school absenteeism system generated simultaneous signals for the varicella outbreak, but 3 days in advance for the mumps outbreak and 2-4 days in advance for the influenza-like illness outbreak. The estimated excess protection rates of control measures based on early signals were 0.0%, 19.0-44.1%, and 29.0-37.0% for the three outbreaks, respectively.
  
  
  CONCLUSIONS
  Although not all outbreak control measures can benefit from early signals through school absenteeism surveillance, the effectiveness of early signal-based interventions is obvious. School absenteeism surveillance plays an important role in reducing outbreak spread.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Yunzhou
authors.initials: Y
authors.lastname: Fan
authors.affiliation: Department of Health Surveillance and Management, Futian District Center for Disease Control and Prevention of Shenzhen, Guangdong, China.
authors.forename: Mei
authors.initials: M
authors.lastname: Yang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Jiang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Wenwen
authors.initials: W
authors.lastname: Yang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Zhixia
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Division of Global Health (IHCAR), Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Weirong
authors.initials: W
authors.lastname: Yan
authors.affiliation: Division of Global Health (IHCAR), Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Vinod K
authors.initials: VK
authors.lastname: Diwan
authors.affiliation: School of Public Health, Fudan University, Shanghai, China.
authors.forename: Biao
authors.initials: B
authors.lastname: Xu
authors.affiliation: Institute of Public Health, Heidelberg University, Heidelberg, Germany.
authors.forename: Hengjin
authors.initials: H
authors.lastname: Dong
authors.affiliation: Future Position X (FPX), Gävle, Sweden.
authors.forename: Lars
authors.initials: L
authors.lastname: Palm
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Shaofa
authors.initials: S
authors.lastname: Nie
mesh_terms: D000041:Absenteeism; D000293:Adolescent; D000465:Algorithms; D044466:Asian People; D002644:Chickenpox / Q000208:ethnology / Q000517:prevention & control*; D002648:Child; D002675:Child, Preschool; D002681:China / Q000453:epidemiology; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control; D005260:Female; D005843:Geography; D006801:Humans; D007251:Influenza, Human / Q000208:ethnology / Q000517:prevention & control*; D008297:Male; D008962:Models, Theoretical; D009107:Mumps / Q000208:ethnology / Q000517:prevention & control*; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D011159:Population Surveillance / Q000379:methods; D012423:Rural Health / Q000706:statistics & numerical data*; D012574:Schools