Īs such, there is potential for COVID-19 to eventually become endemic in the global population, which means that the SARS-CoV-2 virus could be here to stay and coexist with humanity. These are namely the B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta) and B.1.1.529 (Omicron) variants which had become the globally dominant strains over different periods of the pandemic, typically lasting over several months before the emergence of the next dominant VOC ( Fig 1).įig 1. Our study focuses on five key variants of concern (VOCs) that have become a global concern due to high transmissibility and virulence. For this purpose, this study seeks to predict when and where the virus could mutate next, which can then be passed to virologists for further analysis. With the constant emergence of new dominant strains, we require regular vaccine shots to boost our immunity, much like the seasonal flu. This will allow vaccine manufacturers to remain a step ahead of the virus, enabling them to preemptively prepare for quick adaption of the vaccine production process, potentially saving countless lives. Thus, it has become increasingly imperative to predict when and where the next mutation would occur ahead of the actual mutation. The media has increasingly highlighted evidence of an alarming increase in the number of “breakthrough cases”, whereby individuals are still becoming reinfected by the virus despite having already been vaccinated against it. There remains a constant race between the production of effective vaccines versus the mutation of new virus strains that could threaten to render existing vaccines obsolete. This is of critical importance and urgency especially during widespread outbreaks, such as the COVID-19 pandemic that still ravages parts of the world even over two years since the outbreak. Through careful genome sequencing, researchers seek to gain a better understanding of this lethal foe.Īccurate estimates of virus mutation rates play an integral role in understanding the evolution of viruses and the tactics to combat them. Scientists all around the world have worked tirelessly to combat the SARS-CoV-2 virus which caused this deadly disease. As of this writing, there have been more than 200 million recorded infections, resulting in over 5 million deaths. The COVID-19 pandemic has wreaked havoc across all corners of the globe ever since its initial emergence at the end of 2019. This research could also potentially pave the way for future work in adopting similar spatial random process models and advanced spatial pattern recognition algorithms in order to characterize mutations on other different kinds of virus families. Our findings contribute interesting insights to the underlying biological mechanism of SARS-CoV-2 mutations, bringing us one step closer to improving the accuracy of existing mutation prediction models. In particular, our spatial gene sequence results reveal some novel biological insights on the characteristic distribution of mutation inter-occurrence distances, which display a notable pattern similar to other natural diseases. The time-series results reveal distinct asymmetries in mutation rate and propensities among different nucleotides and across different strains, with a mean mutation rate of approximately 2 mutations per month. Our experiment focuses on five key variants of concern that had become a global concern due to their high transmissibility and virulence. In the time-series model, a Markov Chain embedded Poisson random process characterizes the mutation rate matrix, while the spatial gene sequence model delineates the distribution of mutation inter-occurrence distances. This study analyzes the SARS-CoV-2 genome sequence mutations by modeling its nucleotide mutations as a stochastic process in both the time-series and spatial domain of the gene sequence.
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