title: Using a Negative Binomial Regression Model for Early Warning at the Start of a Hand Foot Mouth Disease Epidemic in Dalian, Liaoning Province, China.
abstract: BACKGROUND
  The hand foot and mouth disease (HFMD) is a human syndrome caused by intestinal viruses like that coxsackie A virus 16, enterovirus 71 and easily developed into outbreak in kindergarten and school. Scientifically and accurately early detection of the start time of HFMD epidemic is a key principle in planning of control measures and minimizing the impact of HFMD. The objective of this study was to establish a reliable early detection model for start timing of hand foot mouth disease epidemic in Dalian and to evaluate the performance of model by analyzing the sensitivity in detectability.
  
  
  METHODS
  The negative binomial regression model was used to estimate the weekly baseline case number of HFMD and identified the optimal alerting threshold between tested difference threshold values during the epidemic and non-epidemic year. Circular distribution method was used to calculate the gold standard of start timing of HFMD epidemic.
  
  
  RESULTS
  From 2009 to 2014, a total of 62022 HFMD cases were reported (36879 males and 25143 females) in Dalian, Liaoning Province, China, including 15 fatal cases. The median age of the patients was 3 years. The incidence rate of epidemic year ranged from 137.54 per 100,000 population to 231.44 per 100,000population, the incidence rate of non-epidemic year was lower than 112 per 100,000 population. The negative binomial regression model with AIC value 147.28 was finally selected to construct the baseline level. The threshold value was 100 for the epidemic year and 50 for the non- epidemic year had the highest sensitivity(100%) both in retrospective and prospective early warning and the detection time-consuming was 2 weeks before the actual starting of HFMD epidemic.
  
  
  CONCLUSIONS
  The negative binomial regression model could early warning the start of a HFMD epidemic with good sensitivity and appropriate detection time in Dalian.
authors.affiliation: Dalian Center for Disease Control and Prevention, Liaoning Province, PR China.
authors.forename: Qingyu
authors.identifier: http://orcid.org/0000-0001-7889-6968
authors.initials: Q
authors.lastname: An
authors.affiliation: Dalian Center for Disease Control and Prevention, Liaoning Province, PR China.
authors.forename: Jun
authors.initials: J
authors.lastname: Wu
authors.affiliation: Dalian Center for Disease Control and Prevention, Liaoning Province, PR China.
authors.forename: Xuesong
authors.initials: X
authors.lastname: Fan
authors.affiliation: Dalian Center for Disease Control and Prevention, Liaoning Province, PR China.
authors.forename: Liyang
authors.initials: L
authors.lastname: Pan
authors.affiliation: Dalian Center for Disease Control and Prevention, Liaoning Province, PR China.
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D002681:China; D058872:Epidemics / Q000517:prevention & control / Q000706:statistics & numerical data*; D006232:Hand, Foot and Mouth Disease / Q000453:epidemiology*; D006801:Humans