Table 3

Districts in Sri Lanka for which inclusion of a covariate in the mean term of the best (S)ARIMA model tested improved the mean absolute relative error of out of series prediction at forecasting horizons of 1 to 4 months ahead.

District
Horizon (months)
Lag (months)
covariate
Improvement (%)

Badulla
4
4
rainy day index, with a separate coefficient for each calendar month
6.5
Gampaha
3
4
logarithmically transformed total monthly rainfall (mm)
3.8
Gampaha
4
4
logarithmically transformed total monthly rainfall (mm)
4.5
Mannar
1
2
logarithmically transformed total monthly rainfall (mm)
5.2
Moneragala
2
2
monthly rainfall factored into quintiles
4.1
Moneragala
2
3
rainy day index
4.6
Moneragala
3
3
rainy day index
3.2
Mullaitivu
1
1
monthly rainfall factored into quintiles
2.6



logarithmically transformed total monthly rainfall (mm), with a separate

Ratnapura
3
4
coefficient for each calendar month
3.9



logarithmically transformed total monthly rainfall (mm), with a separate

Ratnapura
4
4
coefficient for each calendar month
3.6



logarithmically transformed total monthly rainfall (mm), with a separate

Trincomalee
2
2
coefficient for each calendar month
8.4



logarithmically transformed total monthly rainfall (mm), with a separate

Trincomalee
3
3
coefficient for each calendar month
9.2
Vavuniya
4
4
logarithmically transformed total monthly rainfall (mm)
2.5

Briët et al. Malaria Journal 2008 7:76   doi:10.1186/1475-2875-7-76

Open Data