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In Immunoinformatics (Amsterdam, Netherlands)

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Gazeau Sonia, Deng Xiaoyan, Ooi Hsu Kiang, Mostefai Fatima, Hussin Julie, Heffernan Jane, Jenner Adrianne L, Craig Morgan


COVID-19, SARS-CoV-2, computational modelling, immunopathology, machine learning, mathematical modelling, population genetics, within-host dynamics