Publications by authors named "Sonia Gazeau"
Article Synopsis
- The COVID-19 pandemic has severely impacted immunosuppressed individuals, such as solid organ transplant recipients and those undergoing cancer treatment, leading to worse health outcomes and higher mortality rates.
- Due to challenges in studying these vulnerable populations, researchers created a mathematical model to simulate immune responses and analyzed virtual patient cohorts that mirrored clinical data from cancer and immunosuppressed patients.
- The model revealed that severe cases in these groups exhibited reduced CD8+ T cells, delayed type I interferon peaks, and higher tissue damage, suggesting that immune dysfunction is a critical factor in COVID-19 severity for cancer patients.
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.
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