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Table 2 The fitting results of the proposed models in Tengchong and Longling

From: Inference and prediction of malaria transmission dynamics using time series data

County Model RMSE MSE MAE R-squared
Tengchong Weather-driven model 9.2986 86.4642 7.9169 0.8129
  Data-driven model 5.2562 27.6277 3.5287 0.9323
Longling Weather-driven model 3.0427 9.2577 2.3347 0.6003
  Data-driven model 2.1368 4.5659 1.6566 0.7609
  1. 1RMSE: root-mean-square error; MSE: mean squared error; MAE: mean absolute error