title: Statistical monitoring of the hand, foot and mouth disease in China.
abstract: In a period starting around 2007, the Hand, Foot, and Mouth Disease (HFMD) became wide-spreading in China, and the Chinese public health was seriously threatened. To prevent the outbreak of infectious diseases like HFMD, effective disease surveillance systems would be especially helpful to give signals of disease outbreaks as early as possible. Statistical process control (SPC) charts provide a major statistical tool in industrial quality control for detecting product defectives in a timely manner. In recent years, SPC charts have been used for disease surveillance. However, disease surveillance data often have much more complicated structures, compared to the data collected from industrial production lines. Major challenges, including lack of in-control data, complex seasonal effects, and spatio-temporal correlations, make the surveillance data difficult to handle. In this article, we propose a three-step procedure for analyzing disease surveillance data, and our procedure is demonstrated using the HFMD data collected during 2008-2009 in China. Our method uses nonparametric longitudinal data and time series analysis methods to eliminate the possible impact of seasonality and temporal correlation before the disease incidence data are sequentially monitored by a SPC chart. At both national and provincial levels, our proposed method can effectively detect the increasing trend of disease incidence rate before the disease becomes wide-spreading.
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, Florida 32611, U.S.A.
authors.forename: Jingnan
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, Florida 32611, U.S.A.
authors.forename: Yicheng
authors.initials: Y
authors.lastname: Kang
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, Florida 32611, U.S.A.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Department of Biostatistics, University of Florida, Gainesville, Florida 32611, U.S.A.
authors.forename: Peihua
authors.initials: P
authors.lastname: Qiu
mesh_terms: D002681:China / Q000453:epidemiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000517:prevention & control / Q000706:statistics & numerical data*; D006232:Hand, Foot and Mouth Disease / Q000175:diagnosis* / Q000453:epidemiology* / Q000517:prevention & control; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012621:Seasons; D012680:Sensitivity and Specificity
keywords: ARIMA model; Bootstrap; Disease surveillance and prevention; Early detection; Nonparametric longitudinal data analysis; Statistical process control; Time series