The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

Abstract

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.

Keywords: COVID-19; Computational modelling; Immunopathology; Machine learning; Mathematical modelling; Population genetics; SARS-CoV-2; Within-host dynamics.

Graphical abstract

Fig. 1

Computational approaches to understanding the immune response and immunopathology in COVID-19 across scales. Beginning at the level of genes, the application of population genetics techniques enables the quantification of SARS-CoV-2 mutational patterns and dynamics (Section 3). Bioinformatics integrates computational and analytical methods to describe and interpret biological data through a variety of approaches, including dimensionality reduction (Section 4). Mathematical and computational modelling are means to quantitatively study and predict the immune response and immunopathology in COVID-19 (Section 2). Machine learning algorithms are able to effectively process multidimensional data and provide insights into complex systems that contribute to vaccine development and drug repurposing for COVID-19 (Section 5).