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Table 2 Autocorrelation and partial correlation of monthly malaria incidence, and the fitting and predictive residuals of the model

From: Spatiotemporal distribution of malaria and the association between its epidemic and climate factors in Hainan, China

Lag Monthly malaria incidence   Fitting residual   Predictive residual
  AC PAC LB P   AC PAC LB P   AC PAC LB P
1 0.84 0.84 102.48 < 0.01   -0.06 -0.06 0.20 0.66   0.29 0.29 1.25 0.26
2 0.60 -0.33 155.20 < 0.01   -0.04 -0.05 0.31 0.86   -0.33 -0.45 3.12 0.21
3 0.30 -0.33 168.51 < 0.01   0.06 0.06 0.54 0.91   -0.13 0.18 3.45 0.33
4 0.03 -0.07 168.64 < 0.01   0.03 0.03 0.59 0.97   0.17 0.01 4.04 0.40
5 -0.19 -0.05 173.85 < 0.01   0.04 0.05 0.67 0.99   0.07 -0.03 4.17 0.53
6 -0.27 0.17 185.02 < 0.01   0.01 0.02 0.68 0.99   -0.02 0.09 4.18 0.65
7 -0.24 0.12 194.14 < 0.01   0.05 0.06 0.88 0.99   -0.17 -0.26 5.11 0.65
8 -0.11 0.15 196.02 < 0.01   -0.04 -0.04 0.98 0.99   -0.28 -0.18 8.47 0.39
9 0.09 0.18 197.33 < 0.01   0.07 0.07 1.32 0.99   -0.19 -0.16 10.47 0.31
10 0.32 0.20 213.76 < 0.01   -0.14 -0.14 2.62 0.99   0.06 -0.02 10.80 0.37
11 0.51 0.08 254.12 < 0.01   0.06 0.06 2.92 0.99      
12 0.56 -0.18 303.34 < 0.01   -0.22 -0.25 6.51 0.89      
  1. AC: autocorrelation coefficient. PAC: partial autocorrelation coefficient. LB: Ljung-Box Q Statistic. Lag: the number of lagged months. For the monthly malaria incidence, P < 0.05 indicates a strong autocorrelation of monthly malaria incidence. For the fitting and predictive residuals, P > 0.05 indicates that the model extracted the information sufficiently and had good prediction validity.