|
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
| | | | | |
- 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.
