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Predicting the efficacy of COVID-19 vaccines
Understanding immune responses induced by COVID-19 vaccines is critical for vaccine development and usage. Vaccine-induced neutralising antibody (NAb) responses provide the first line of defence to prevent infections. Here, we developed a mechanistic, multi-scale mathematical model that provides quantitative links between COVID-19 vaccine efficacies and NAb responses. To describe the inter-patient variability in NAb responses, we hypothesised that the entire range of NAbs that can be generated would constitute a shape space, with individual responses being random samples from this space. By analysing published in vitro dose-response curves of ~80 NAbs, we constructed the shape space, and by sampling subsets of NAbs from this space, we recapitulated the responses observed in convalescent patients. We developed a model of within-host SARS-CoV-2 dynamics, performed virtual clinical trials, and quantitatively predicted vaccine efficacies against the original SARS-CoV-2 strain based on clinical trial data. Our study suggests the mechanistic origins of COVID-19 vaccine efficacies against the original strain. The model needs to be advanced to predict efficacy against variants.