Machine-learning-aided multiplexed nanoplasmonic biosensor for COVID-19 population immunity profiling

Abstract

Serological population surveillance plays a crucial role in monitoring the spread, evolution, and outbreak risks of infectious diseases, including COVID-19. However, current commercial rapid serological tests fall short of capturing complex humoral immune response from a diverse population. On the other hand, access to laboratory-based diagnostic tests can be challenging in pandemic settings. To address these issues, we report a machine-learning (ML)-aided nanoplasmonic biosensor that can simultaneously quantify antibodies against the ancestral strain and Omicron variants of SARS-CoV-2 with epitope resolution. Our approach is based on a multiplexed, rapid, and label-free nanoplasmonic biosensor, which can detect past infection and vaccination status and is sensitive to SARS-CoV-2 variants. After training an ML model with antigen-specific antibody datasets from four COVID-19 immunity groups (naïve, convalescent, vaccinated, and convalescent-vaccinated), we tested our approach on 100 blind blood samples collected in Dane County, WI. Our results are consistent with public epidemiological data, demonstrating that our user-friendly and field-deployable nanobiosensor can capture community-representative public health trends and help manage COVID-19 and future outbreaks.