An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka
Fig 7
Prospective validation of the hybrid model (SEIR-LSTM) and the pure machine learning model (LSTM) on average weekly case case data for all districts in 2019 (A) and per district aggregated mean absolute error (MAE) of the SEIR-LSTM model (B).
In (A) the X-axis represents the time in weeks and the Y-axis represents the newly infected cases. The blue shaded area represents the prediction interval of the LSTM model. The MAE over all time steps of the hybrid model and LSTM respectively are 491 and 527. The light red shaded area represents the confidence interval of the hybrid model (see uncertainty qualification for details). The choropleth map (B) shows the MAE of the hybrid model forecasts for each geographical district where X- and Y-axes correspond to longitude and latitude. Comparison figures as figure (A) for all the districts can be found in Fig C and Fig E in S1 Text, S3 and S5 Figs.