  The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated
vaccine development to prevent the spread of the virus and control the disease.
Given the sustained high infectivity and evolution of SARS-CoV-2, there is an
ongoing interest in developing COVID-19 serology tests to monitor
population-level immunity. To address this critical need, we designed a
paper-based multiplexed vertical flow assay (xVFA) using five structural
proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in
COVID-19 immunity levels. Our platform not only tracked longitudinal immunity
levels but also categorized COVID-19 immunity into three groups: protected,
unprotected, and infected, based on the levels of IgG and IgM antibodies. We
operated two xVFAs in parallel to detect IgG and IgM antibodies using a total
of 40 uL of human serum sample in <20 min per test. After the assay, images of
the paper-based sensor panel were captured using a mobile phone-based
custom-designed optical reader and then processed by a neural network-based
serodiagnostic algorithm. The trained serodiagnostic algorithm was blindly
tested with serum samples collected before and after vaccination or infection,
achieving an accuracy of 89.5%. The competitive performance of the xVFA, along
with its portability, cost-effectiveness, and rapid operation, makes it a
promising computational point-of-care (POC) serology test for monitoring
COVID-19 immunity, aiding in timely decisions on the administration of booster
vaccines and general public health policies to protect vulnerable populations.
