Time Series Analyses of Hand, Foot and Mouth Disease Integrating Weather Variables

摘要: Background The past decade witnessed an increment in the incidence of hand foot mouth disease (HFMD) Pacific Asian region; specifically, Guangzhou China. This emphasized requirement early warning system designed to allow medical community better prepare for outbreaks and thus minimize number fatalities. Methods Samples from 1,556 inpatients (hospitalized) 11,004 outpatients (non-admitted) diagnosed with HFMD were collected this study January 2009 October 2013. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied establish high predictive outpatient as well three viral serotypes (EV71, Pan-EV CA16). To integrate climate variables data analyses, eight simultaneously obtained during period. Significant variable identified by correlation analyses executed improve time series modeling external repressors. Results Among HFMD, 248 (15.9%) affected EV71, 137 (8.8%) Pan-EV+, 436 (28.0%) CA16. Optimal Univariate SARIMA identified: (2,0,3)(1,0,0)52 inpatients, (0,1,0)(0,0,2)52 (1,0,1)(0,0,1)52; CA16, (1,0,1)(0,0,0)52; Pan-EV, (1,0,1)(0,0,0)52). Using our independent variable, precipitation (PP) first be associated (r = 0.211, P 0.001), CA16-serotype 0.171, 0.007) 0.214, 0.01) partial then shown a significant lag cross-autocorrelation analyses. However, inclusion PP [lag -3 week] repressor showed moderate impact on performance described here-in. Conclusion Climate patterns incidences have been strongly correlated. The developed here can helpful tool developing HFMD.