Computational and Mathematical Methods in Medicine / 2015 / Article / Tab 4

Research Article

Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses

Table 4

Summary of ARIMA model fitting parameters in the central region during 2009–2014.

ModelFitPred.Climate variables
RMSEAICRMSEVarsCoef. value

() ARIMA(1, 0, 2)(1, 0, 0)120.455089.740.7837
() ARIMAX(1, 0, 2)(1, 0, 0)12 with Rainfall0.442588.010.7810Rainfall (lag 0)−0.12340.046
() ARIMAX(1, 0, 2)(1, 0, 0)12 with Rainfall0.442086.420.8339Rainfall (lag 1)0.13740.0241
() ARIMAX(1, 0, 2)(1, 0, 0)12 with Rainfall0.458489.990.8045Rainfall (lag 2)0.06430.1989
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.434482.360.7042 (lag 3)−0.28060.8523
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.422484.130.8139 (lag 4)3.97270.0356
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.453691.470.7989 (lag 0)−0.47270.6001
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.444286.950.7586 (lag 1)1.99880.0392
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.454991.730.7843 (lag 0)−0.04690.6372
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.417079.370.8880 (lag 1)2.03530.0003
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.443985.900.6760 (lag 2)−1.35070.0256
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.454491.590.7911 (lag 0)−0.25350.7034
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.435584.520.7896 (lag 1)1.79950.0090
() ARIMAX(1, 0, 2)(1, 0, 0)12 with 0.457189.210.7390 (lag 2)−0.71040.3375
() ARIMAX(1, 0, 2)(1, 0, 0)12 with , 0.399874.980.7507 (lag 4)
(lag 1)
4.4553
2.4223
0.0001
0.0039
() ARIMAX(1, 0, 2)(1, 0, 0)12 with , 0.392270.780.9337 (lag 4)
(lag 1)
3.8566
1.8012
0.0267
0.0026
() ARIMAX(1, 0, 2)(1, 0, 0)12 with , 0.378672.820.5792 (lag 4)
(lag 2)
3.8699
−1.9457
<0.0001
<0.0001
() ARIMAX(1, 0, 2)(1, 0, 0)12 with , 0.378672.140.8027 (lag 4)
(lag 1)
4.9256
2.0620
<0.0001
<0.0001

ARIMAX: autoregressive integrated moving average with input series; fit: fitting results; RMSE: root mean square error; AIC: Akaike’s Information Criterion; Pred.: prediction of ARIMA model; Coef.: coefficient of climate variables; lag: time lag of climate variables.

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