Sequence-Based Transmission Modelling of SARS-CoV-2: A Tree-Free Approach Using Hamming Distance

Music City Center 

The spatiotemporal dynamics of SARS-CoV-2 transmission are influenced by factors such as human mobility, cumulative incidence, and vaccination coverage. Understanding how these factors shape transmission is essential for designing effective public health strategies. Traditional phylogenetic methods for inferring viral spread are often computationally intensive and unreliable when genetic divergence is low. This research introduces a tree-free modelling framework that leverages Hamming distance-a direct measure of genetic similarity between viral sequences-to estimate the impact of epidemiological factors on transmission and susceptibility. We introduce a likelihood-based framework and conduct simulation studies to demonstrate that the model reliably estimates parameters associated with these factors while addressing key challenges of phylogenetic approaches. Applying this method to SARS-CoV-2 genomic data from Washington State during the Delta wave of 2021, we find that higher cumulative incidence and vaccination rates substantially reduce population susceptibility, though with diminishing returns at higher levels. This work demonstrates the effectiveness of Hamming distance.

Infectious disease

COVID-19

Statistical Modelling

Hamming distance

Spatial transmission 

Section on Statistics in Epidemiology