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

Table 5

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

Model	Fit		Pred.	Climate variables
	RMSE	AIC	RMSE	Vars	Coef.	value
() ARIMA(1, 0, 2)(0, 0, 1)12	0.3486	57.74	0.2496
() ARIMAX(1, 0, 2)(0, 0, 1)12 with	0.3415	56.65	0.3062	(lag 3)	−1.8054	0.6023
() ARIMAX(1, 0, 2)(0, 0, 1)12 with	0.3471	56.46	0.2235	(lag 4)	2.6311	0.4453
() ARIMAX(1, 0, 2)(0, 0, 1)12 with	0.3480	58.44	0.2748	(lag 1)	−1.5168	0.6160
() ARIMAX(1, 0, 2)(0, 0, 1)12 with ,	0.3483	57.49	0.2589	(lag 4); (lag 1)	3.3490; 3.6432	0.3580; 0.3640

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.