Background
Methods
Study area
Malaria data
Meteorological data
ARIMA Modelling Methods
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▪ p and P- the auto regressive and seasonal autoregressive, respectively;
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▪ d and D- the non-seasonal differences and seasonal differencing, respectively;
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▪ q and Q- the moving average parameters and seasonal moving average parameters, respectively.
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▪ s representing the length of the seasonal period.
ARIMAX Modelling Methods
Results
Overall malaria incidence
Time-series Forecasting Models
Districts | Model | Malaria cases | Error percentage | |
---|---|---|---|---|
Actual cases | Predicted case | |||
Chukha | (2,1,1)(0,1,1)12 | 80 | 77.09 | 3.64 |
Dagana | (1,1,1)(0,1,1)12 | 51 | 49.76 | 2.43 |
Pemagatshel | (1,1,1)(0,1,1)12 | 41 | 44.17 | -7.72 |
Samdrup Jongkhar | (2,1,1)(0,1,1)12 | 366 | 356.78 | 2.52 |
Samtse | (1,1,0)(0,1,1)12 | 76 | 57.16 | 24.80 |
Sarpang | (1,1,1)(0,1,1)12 | 386 | 383.30 | 0.70 |
Zhemgang | (1,1,1)(0,1,1)12 | 7 | 6.59 | 5.90 |
All districts | (2,1,1)(0,1,1)12 | 1007.00 | 1088.80 | -8.12 |
District | Forecasted malaria cases | Total | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | ||
Chukha | 2 | 1 | 2 | 11 | 4 | 5 | 7 | 3 | 3 | 5 | 4 | 4 | 50 |
Dagana | 1 | 1 | 1 | 4 | 2 | 1 | 2 | 1 | 4 | 3 | 1 | 3 | 24 |
Pemagatshel | 0 | 1 | 0 | 5 | 0 | 2 | 0 | 1 | 1 | 2 | 0 | 0 | 12 |
Samdrup Jongkhar | 3 | 2 | 3 | 18 | 11 | 7 | 19 | 15 | 7 | 21 | 13 | 12 | 131 |
Samtse | 0 | 0 | 0 | 3 | 2 | 1 | 3 | 2 | 0 | 0 | 0 | 0 | 11 |
Sarpang | 14 | 11 | 9 | 39 | 25 | 22 | 33 | 42 | 36 | 38 | 47 | 34 | 350 |
Zhemgang | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 2 | 1 | 1 | 1 | 9 |
All district | 19 | 16 | 15 | 82 | 45 | 40 | 68 | 65 | 53 | 70 | 67 | 55 | 595 |
District | Forecasted malaria cases | Total | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | ||
Chukha | 5 | 5 | 5 | 14 | 8 | 9 | 11 | 8 | 7 | 10 | 9 | 9 | 101 |
Dagana | 2 | 2 | 2 | 5 | 3 | 2 | 3 | 2 | 5 | 4 | 2 | 4 | 35 |
Pemagatshel | 0 | 1 | 0 | 5 | 0 | 2 | 0 | 1 | 1 | 2 | 0 | 0 | 13 |
Samdrup Jongkhar | 10 | 10 | 11 | 27 | 21 | 18 | 30 | 26 | 19 | 33 | 27 | 26 | 258 |
Samtse | 0 | 0 | 0 | 2 | 1 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 5 |
Sarpang | 48 | 47 | 48 | 80 | 69 | 68 | 81 | 92 | 89 | 94 | 105 | 95 | 915 |
Zhemgang | 1 | 1 | 1 | 2 | 1 | 1 | 4 | 2 | 3 | 2 | 2 | 2 | 22 |
All district | 67 | 67 | 70 | 140 | 106 | 104 | 136 | 137 | 128 | 149 | 149 | 140 | 1393 |
Prediction Models with Covariates
Models | Covariates | Districts* | |||||
---|---|---|---|---|---|---|---|
1 | 2 | 4 | 5 | 6 | All districts | ||
I
|
Cases
| 0.000 | 0.000 | 0.000 | 0.04 | 0.000 | |
BIC
|
Temp min
|
792.3911
|
1524.363
|
1337.595
|
1731.596
|
1812.401
| |
II
|
Cases,
| 0.002 | 0.000 | 0.000 | 0.000 | 0.033 | 0.000 |
BIC
|
Temp min, Hum
|
1107.768
|
792.5185
|
1528.834
|
1341.371
|
1735.813
|
1815.411
|
III
|
Cases,
| 0.001 | 0.000 | 0.000 0.000 | 0.001 0.004 | 0.038 | 0.000 |
BIC
|
Temp max, Rainfall
|
1112.112
|
784.5172
|
1505.547
|
1343.952
|
1734.896
|
1812.601
|
IV
|
Cases,
| 0.000 | 0.001 | ||||
Temp max, Rainfall
| 0.004 | 0.000 | 0.038 | 0.042 | 0.000 | ||
Hum
| |||||||
BIC
|
1126.668
|
796.214
|
1514.975
|
1345.209
|
1744.511
|
1817.437
| |
V
|
Cases,
| 0.000 | |||||
Temp max,
| 0.001 | ||||||
Temp min, Rainfall,
| 0.001 | ||||||
Hum
| |||||||
BIC
|
789.4534
|
1514.97
|