Figure 7:

Comparison of weekly COVID-19 infection forecasting performance among Persistence model, CDC model, and our proposed model. (A) The RMSE and relative RMSE values of the three models at the county level. (B) The comparison of prediction errors among these three models at the state level. Our proposed FIGI-Net model outperforms the Persistence model and CDC model in terms of lower prediction RRMSE errors. This indicates its enhanced capability to capture the complex dynamics of COVID-19 infection spread, with approximate 4.76% averaged reduction in errors observed across various prediction horizons. (C) The error reduction comparison between the CDC model and FIGI-Net. At the county level, FIGI-Net outperforms the CDC model, with errors approximately 58.5% lower than those of the Persistence model. At the state level, FIGI-Net continues to provide a 13% lower error reduction compared to the CDC model.