High-Performance Computing Center, Oklahoma State University, Stillwater, United States
Veniamin A Borin
High-Performance Computing Center, Oklahoma State University, Stillwater, United States; Department of Physiological Sciences, Oklahoma State University, Stillwater, United States
Huan Wang
University College London School of Pharmacy, Pharmaceutical and Biological Chemistry, London, United Kingdom
University College London School of Pharmacy, Pharmaceutical and Biological Chemistry, London, United Kingdom; University College London Centre for Advanced Research Computing, London, United Kingdom
High-Performance Computing Center, Oklahoma State University, Stillwater, United States; Department of Physiological Sciences, Oklahoma State University, Stillwater, United States
COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate combined with high rates of transmission and fatality can cause a deadly worldwide pandemic in a matter of weeks (Plato et al., 2021). Apart from vaccines and post-infection treatment options, strategies for preparedness will be vital in responding to the current and future pandemics. Therefore, there is wide interest in approaches that allow predictions of increase in infections (‘surges’) before they occur. We describe here real-time genomic surveillance particularly based on mutation analysis, of viral proteins as a methodology for a priori determination of surge in number of infection cases. The full results are available for SARS-CoV-2 at http://pandemics.okstate.edu/covid19/, and are updated daily as new virus sequences become available. This approach is generic and will also be applicable to other pathogens.
