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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.

From: Models for short term malaria prediction in Sri Lanka

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