Reconstructing the evolutionary history of closely-related viral strains enables the identification and epidemiological linkage of infected patients in an outbreak, but phylogenetic inference is often too computationally time-consuming to be performed in real-time on large datasets. Instead, inferring the epidemiological linkage from pairwise distances between sequences is being used. However, CPUs are unable to provide the real-time process of the ultra-large datasets. Providing high parallelism, FPGAs are well-suited for pairwise distance computations. In this work, we introduce, FANTAIL, a highly parallelized FPGA-based accelerator for computing pairwise distance for viral transmission clustering based on Tamura-Nei 93 (TN93) model.