Generally, phylogenemic inference uses sequence data to build phylogenetic tree to infer epidemiological parameters. However, when there is not enough genetic diversity, for example during the early spread of virus, there is significant amount of uncertainty in phylogenetic tree. To resolve this uncertainty, a huge amount of computation may be required. So we just suggest a phylogenemic inference that is tree-free to avoid some uncertainty issues. Our inference method is based on the idea that as mutations are accumulated in the viral population while virus spreads, how they spread will leave signatures in the genetic diversity pattern. So we thought maybe we can use this genetic diversity to infer the epidemiological parameters. We summarized this genetic diversity pattern with the number of segregating sites, which is the number of polymorphic sites, in example. To see if different epidemiological parameters really have different segregating site trajectory, we simulated different scenarios, and different scenarios have different segregating site trajectories. Using a model that can jointly simulate the epidemiological and evolutionary process that we used here, we jointly estimated the basic reproduction rate and the time of index case from mock data and the SARS-CoV-2 sequence data, sampled from France. For the mock data, we could successfully recover the true parameter used to generate mock data, and for France data, our estimate for basic reproduction rate and the time of index case aligned with other studies. So when the genetic diversity is slow, segregating site-based inference could be a great alternative for a tree-based inference. Thank you.