An innovative hybrid approach using quantum computing and genomic data analysis for predictive Covid-19 surveillance
Abstract
The global response to the COVID-19 pandemic exposed the limitations of an epidemiological model that is fundamentally reactive, where clinical and public health responses to the virus's evolution can only occur after new variants are developed and disseminated. The constant mutational evolution of the viral Spike protein means that a classical surveillance system has little chance of being able to predict new COVID-19 variant emergence. As such, this study proposes an important shift toward proactive prediction and proves it is possible to predict mutations before they emerge, using a hybrid approach that combines genomic data analysis and new potentials from quantum computing. Clusters analysis of SARS-CoV-2 genomic sequences from France, Germany, Italy, Spain, and Portugal has identified three major clusters of Spike protein mutations, and preliminary results suggest 19 universal mutations from them. It consistently was found that mutation dynamics in Italy were comparatively negatively correlated to mutation dynamics in the other countries in Europe. This finding bolsters the importance of a thorough molecular analysis and prototype relations that far exceed time-based chronology. Finally, this work shows that quantum computing is a practical solution to tackling the exponential complexity of this problem on a global scale. It also provides a path for future vaccine design in specific regions, heralding a new era in preventive medicine.
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