TABLE II. Performance Comparison of NPI-LSTM Predictor With Baselines.

Method	Norm. Case	Raw Case	Mean Rank	1-step
MLP	2.47±1.22	1089126±540789	3.19±0.09	0.769±0.033
RF	0.95±0.05	221308±8717	1.98±0.10	0.512±0.000
SVR	0.71±0.00	280731±0	1.76±0.09	0.520±0.000
Linear	0.64±0.00	176070±0	1.63±0.09	0.902±0.000
NPI-LSTM	0.42±0.04	154194±14593	1.46±0.08	0.510±0.001

This table shows results along the four metrics described in Section V-B with mean and standard error over 10 trials. Interestingly, although RF and SVR do quite well in terms of the loss they were trained on (1-step MAE), the simple linear model outperforms them substantially on the metrics that require forecasting beyond a single day, showing the difficulty that off-the-shelf nonlinear methods have in handling such forecasting. In contrast, with the extensions developed specifically for the epidemic modeling case, the NPI-LSTM methods outperforms the baselines on all metrics.