Background:　Blood　donation　forecasting　is　a　critical　part　of　blood　supply　chain　management.　However,　few　studies　have　focused　on　modeling　blood　donation　with　different　emergency　factors.　The　purpose　of　this　study　was　to　investigate　the　effects　of　different　emergency　events　on　blood　donation　and　to　build　a　suitable　blood　volume　prediction　model.　Materials　and　methods:　The　amount　of　blood　donation　from　2015　to　December　2021　at　Beijing　Tongzhou　District　Central　Blood　Station　was　selected　as　the　time　series　data.　First,　statistical　methods　were　employed　to　analyze　the　effect　of　weather　and　epidemic　factors　on　blood　donation.　Second,　a　hybrid　model　of　SARIMAX　and　a　neural　network　was　built　to　predict　the　blood　donation　in　the　next　two　weeks　with　two　factors.　Results:　We　identified　significant　differences　in　blood　donations　under　different　emergency　conditions　and　a　high　correlation　between　epidemic　status　and　blood　donations.　In　addition,　the　decision　coefficient　improved　by　60.7%,　and　the　Root　Mean　Square　Error(RMSE)　decreased　by　1.668　when　using　the　hybrid　model　of　SARIMAX　and　the　neural　network,　indicating　that　the　model　was　effective　in　reducing　the　prediction　error　of　blood　donation.　Conclusion:　The　hybrid　model　approach　allows　long-term　forecasting　of　blood　donations　under　emergency　conditions　and　provides　reliable　and　accurate　forecasting　results　for　blood　stations　up　to　2　weeks　in　advance,　facilitating　warnings　on　the　blood　supply　to　relevant　hospitals　and　improving　hospital　treatment　rates　while　reducing　blood　transportation　costs.　(c)　2023　Societe　francaise　de　transfusion　sanguine　(SFTS).　Published　by　Elsevier　Masson　SAS.　All　rights　reserved.