Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data - Miao Miao; Yonghong Ma; Jiao Tan; Renjuan Chen; Ke Men (Scientific Reports)
Abstract Despite the end of the global Coronavirus Disease 2019 (COVID-19) pandemic, the risk factors for COVID-19 severity continue to be a pivotal area of research. Specifically, studying the impact of the genomic diversity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on COVID-19 severity is crucial for predicting severe outcomes. Therefore, this study aimed to investigate the impact of the SARS-CoV-2 genome sequence, genotype, patient age, gender, and vaccination status on the severity of COVID-19, and to develop accurate and robust prediction models. The training set (n = 12,038), primary testing set (n = 4,006), and secondary testing set (n = 2,845) consist of SARS-CoV-2 genome sequences with patient information, which were obtained from Global Initiative on Sharing all Individual Data (GISAID) spanning over four years. Four machine learning methods were employed to construct prediction models. By extracting SARS-CoV-2 genomic features, optimizing model parameters, and integrating models, this study improved the prediction accuracy. Furthermore, Shapley Additive exPlanes (SHAP) was applied to analyze the interpretability of the model and to identify risk factors, providing insights for the management of severe cases. The proposed ensemble model achieved an F-score of 88.842% and an Area Under the Curve (AUC) of 0.956 on the global testing dataset. In addition to factors such as patient age, gender, and vaccination status, over 40 amino acid site mutation characteristics were identified to have a significant impact on the severity of COVID-19. This work has the potential to facilitate the early identification of COVID-19 patients with high risks of severe illness, thus effectively reducing the rates of severe cases and mortality.