Due　to　the　non-linear　and　non-stationary　nature　of　daily　new　2019　coronavirus　disease　(COVID-19)　case　time　series,　existing　prediction　methods　struggle　to　accurately　forecast　the　number　of　daily　new　cases.　To　address　this　problem,　a　hybrid　prediction　framework　is　proposed　in　this　study,　which　combines　ensemble　empirical　mode　decomposition　(EEMD),　fuzzy　entropy　(FE)　reconstruction,　and　a　CNN-LSTM-ATT　hybrid　network　model.　This　new　framework,　named　EEMD-FE-CNN-LSTM-ATT,　is　applied　to　predict　the　number　of　daily　new　COVID-19　cases.　This　study　focuses　on　the　daily　new　case　dataset　from　the　United　States　as　the　research　subject　to　validate　the　feasibility　of　the　proposed　prediction　framework.　The　results　show　that　EEMD-FE-CNN-LSTM-ATT　outperforms　other　baseline　models　in　all　evaluation　metrics,　demonstrating　its　efficacy　in　handling　the　non-linear　and　non-stationary　epidemic　time　series.　Furthermore,　the　generalizability　of　the　proposed　hybrid　framework　is　validated　on　datasets　from　France　and　Russia.　The　proposed　hybrid　framework　offers　a　new　approach　for　predicting　the　COVID-19　pandemic,　providing　important　technical　support　for　future　infectious　disease　forecasting.　©　2024　by　the　authors.