In　this　paper,　based　on　the　Akaike　information　criterion,　root　mean　square　error　and　robustness　coefficient,　a　rational　evaluation　of　various　epidemic　models/methods,　including　seven　empirical　functions,　four　statistical　inference　methods　and　five　dynamical　models,　on　their　forecasting　abilities　is　carried　out.　With　respect　to　the　outbreak　data　of　COVID-19　epidemics　in　China,　we　find　that　before　the　inflection　point,　all　models　fail　to　make　a　reliable　prediction.　The　Logistic　function　consistently　underestimates　the　final　epidemic　size,　while　the　Gompertz＇s　function　makes　an　overestimation　in　all　cases.　Towards　statistical　inference　methods,　the　methods　of　sequential　Bayesian　and　time-dependent　reproduction　number　are　more　accurate　at　the　late　stage　of　an　epidemic.　And　the　transition-like　behavior　of　exponential　growth　method　from　underestimation　to　overestimation　with　respect　to　the　inflection　point　might　be　useful　for　constructing　a　more　reliable　forecast.　Compared　to　ODE-based　SIR,　SEIR　and　SEIR-AHQ　models,　the　SEIR-QD　and　SEIR-PO　models　generally　show　a　better　performance　on　studying　the　COVID-19　epidemics,　whose　success　we　believe　could　be　attributed　to　a　proper　trade-off　between　model　complexity　and　fitting　accuracy.　Our　findings　not　only　are　crucial　for　the　forecast　of　COVID-19　epidemics,　but　also　may　apply　to　other　infectious　diseases.