Figure 8:

Comparison of FIGI-Net model with other state-of-the-art prediction models in predicting weekly and critical time infections. (A) Comparison of the forecasting results among different forecasting models during three different outbreak time periods at the national level. (B) Examples of COVID-19 infection prediction trends of different state-of-the-art forecasting models at the state level. The critical time period, which indicates a significant increase in COVID-19 infections, is highlighted in light grey color. (C) Performance evaluation of the forecasting methods during the critical time periods of COVID-19 infection in 1-week and 2-week horizons across the states. Slope Similarity, RRMSE, and MAPE were measured to assess the prediction number and trend accuracy of each model. Our proposed FIGI-Net model provided lower prediction errors in both 1-week and 2-week horizons during the critical time and may efficiently forecast the infection number and trend direction before the severe transmission of COVID-19. Here we also ranked them from high to low evaluation or error values according to the median values.