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Alignment-based phylogenetics faces challenges to uncover the evolutionarytrajectory and origin of SARS-CoV-2. This study develops a novel alignment-freesystem integrating Fr\'echet distance (Fr) and artificial recurrent neuralnetwork (RNN) to quantitatively reveal the evolutionary trajectory and originof SARS-CoV-2 from more than two millions of genome sequences. Fr measures theevolutionary similarity between a variant and the reference in terms of 84genome features, including 4 single nucleotides, 16 dinucleotides and 64trinucleotides. RNN recognizes the evolutionary trajectory from Fr data.Globally, SARS-CoV-2 evolutionarily deletes its genome to significantly enhanceits infection capacity (tau = -0.64 and p_value= 1.39e-101), and it remarkablydeletes 66 features whereas only gains 18 features. Yet only mutating signaturefeatures such as TTA, GCT and CG increases its infection potential. In organismlevel, variants mutating a single biomarker possess low infectious potential,but those mutating multiple markers dramatically increase their infectioncapacity. Mink coronavirus is the most likely origin of SARS-CoV-2 and theorigin trajectory follows the order: mink, cat, tiger, mouse, hamster, dog,lion, gorilla, leopard, bat, and pangolin. Together, the mink-origin SARS-CoV-2evolves primarily via deletion and mutates multiple loci, causing COVID-19pandemic.

Integrating Fr\'echet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2