Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves over the past years. Therefore, accurate early warning of high-risk variants is vital for epidemic prevention and control. However, detecting high-risk variants through experimental and epidemiological research is time-consuming and often lags behind the emergence and spread of these variants. In this study, HiRisk-Detector a machine learning algorithm based on haplotype network, is developed for computationally early detecting high-risk SARS-CoV-2 variants. Leveraging over 7.6 million high-quality and complete SARS-CoV-2 genomes and metadata, the effectiveness, robustness, and generalizability of HiRisk-Detector are validated. First, HiRisk-Detector is evaluated on actual empirical data, successfully detecting all 13 high-risk variants, preceding World Health Organization announcements by 27 days on average. Second, its robustness is tested by reducing sequencing intensity to one-fourth, noting only a minimal delay of 3.8 days, demonstrating its effectiveness. Third, HiRisk-Detector is applied to detect risks among SARS-CoV-2 Omicron variant sub-lineages, confirming its broad applicability and high ROC-AUC and PR-AUC performance. Overall, HiRisk-Detector features powerful capacity for early detection of high-risk variants, bearing great utility for any public emergency caused by infectious diseases or viruses. This study first validates a correlation between haplotype network features and the risk levels of SARS-CoV-2 variants. Building on this, HiRisk-Detector, a machine learning algorithm, is developed for the early detection of high-risk variants. The effectiveness, robustness, and generalizability of HiRisk-Detector are confirmed using over 7.6 million SARS-CoV-2 genomes. image