Objective　This　study　focuses　on　enhancing　the　precision　of　epidemic　time　series　data　prediction　by　integrating　Gated　Recurrent　Unit　(GRU)　into　a　Graph　Neural　Network　(GNN),　forming　the　GRGNN.　The　accuracy　of　the　GNN　(Graph　Neural　Network)　network　with　introduced　GRU　(Gated　Recurrent　Units)　is　validated　by　comparing　it　with　seven　commonly　used　prediction　methods.Method　The　GRGNN　methodology　involves　multivariate　time　series　prediction　using　a　GNN　(Graph　Neural　Network)　network　improved　by　the　integration　of　GRU　(Gated　Recurrent　Units).　Additionally,　Graphical　Fourier　Transform　(GFT)　and　Discrete　Fourier　Transform　(DFT)　are　introduced.　GFT　captures　inter-sequence　correlations　in　the　spectral　domain,　while　DFT　transforms　data　from　the　time　domain　to　the　frequency　domain,　revealing　temporal　node　correlations.　Following　GFT　and　DFT,　outbreak　data　are　predicted　through　one-dimensional　convolution　and　gated　linear　regression　in　the　frequency　domain,　graph　convolution　in　the　spectral　domain,　and　GRU　(Gated　Recurrent　Units)　in　the　time　domain.　The　inverse　transformation　of　GFT　and　DFT　is　employed,　and　final　predictions　are　obtained　after　passing　through　a　fully　connected　layer.　Evaluation　is　conducted　on　three　datasets:　the　COVID-19　datasets　of　38　African　countries　and　42　European　countries　from　worldometers,　and　the　chickenpox　dataset　of　20　Hungarian　regions　from　Kaggle.　Metrics　include　Average　Root　Mean　Square　Error　(ARMSE)　and　Average　Mean　Absolute　Error　(AMAE).Result　For　African　COVID-19　dataset　and　Hungarian　Chickenpox　dataset,　GRGNN　consistently　outperforms　other　methods　in　ARMSE　and　AMAE　across　various　prediction　step　lengths.　Optimal　results　are　achieved　even　at　extended　prediction　steps,　highlighting　the　model＇s　robustness.Conclusion　GRGNN　proves　effective　in　predicting　epidemic　time　series　data　with　high　accuracy,　demonstrating　its　potential　in　epidemic　surveillance　and　early　warning　applications.　However,　further　discussions　and　studies　are　warranted　to　refine　its　application　and　judgment　methods,　emphasizing　the　ongoing　need　for　exploration　and　research　in　this　domain.