Blunt severity of infection caused by swine-origin H1N1 influenza virus (nH1N1) in a vast majority of individuals highlights the importance of pre-existing immune memory. Due to the evident lack of cross-reactive antibody responses in a large segment of the population, reduced illness may be attributed to pre-existing T-cell immunity directed against epitopes shared between nH1N1 virus and previously circulating strains of inter-pandemic influenza A virus. We sought to identify these epitopes and determine the level of cross-reactivity conferred by CD4+ T cell immune responses. We investigated the degree of CD4+ T cell cross-reactivity between seasonal influenza A (sH1N1 and H3N2) from 1968-2009 and nH1N1 strains. Large scale MHC II based epitope prediction and conservancy analysis on Hemagglutinin (HA) proteins performed by NETMHCIIPAN server and Epitope Conservancy tool, respectively. HA protein sequences used in this analysis were obtained from the Influenza Virus Resource at NCBI. Eighteen MHC II strong binders identified were conserved among the sH1N1 and nH1N1. Each epitope was examined against all the protein sequences that correspond to sH1N1, H3N2 and nH1N1 available in the NCBI. T cell cross-reactivity was estimated about to be ~52%, and maximum conservancy was found between sH1N1 and nH1N1 with a significant correlation (p < 0.05) These results are incorporated into a stochastic continuous time Monte-Carlo Markov-Chain model to simulate the effect of T-cell cross reactivity on disease transmission dynamics. We observed that a prolonged incubation period due to pre-existing immunity could decelerate disease spread, decrease the number of secondary infections, and result in a longer delay in the illness peak of the epidemic. This study demonstrates that prior exposure to sH1N1 strains has conferred substantial level of T-cell cross-reactivity against nH1N1 strains. The findings provide critical information that can be used for vaccine production to cover a broader spectrum of epitopes specific to nH1N1 strains.