Hong Kong University Develops Algorithm to Predict a COVID Vaccine’s Effectiveness Before Delivery

Hong Kong University Develops Algorithm to Predict a COVID Vaccine’s Effectiveness Before Delivery
An algorithm to rapidly predict COVID-19 vaccine effectiveness was developed by CUHK’s Faculty of Medicine and researchers confirm it is 95% accurate. (From left) Ms. Lirong CAO, PhD student, the first author of the article; Prof. Benny ZEE, and Prof. Maggie WANG.
Julia Ye
The Chinese University of Hong Kong’s (CUHK) Faculty of Medicine has developed the world’s first algorithm to rapidly evaluate the effectiveness of vaccines used to combat SARS-CoV-2 variants. According to the article published in the journal of Nature Medicine, the algorithm delivers a 95 percent rate of accuracy when predicting a vaccine’s efficacy and can be used to determine which ingredients would offer more protection.
CUHK’s Faculty of Medicine staff introduced its new bioinformatic algorithm during a July 14 press conference held at the university’s Jockey Club School of Public Health and Primary Care.

In developing the algorithm, the researchers studied the relationship between the “Genetic Distance” (GD) of circulating viruses against the vaccine strain and vaccine effectiveness (VE) against symptomatic infection. This is referred to as the VE-GD framework, which enabled the researchers to examine the binding domain of the viral spike protein receptor, and quickly assess a vaccine’s protective abilities.

The study was led by Associate Professor Maggie H. Wang and Professor Benny C. Zee, who is the director of CUHK’s Center for Clinical Research and Biostatistics. During the study, they analyzed nearly 2 million SARS-CoV-2 virus sequences collected from 31 regions and vaccine efficacy data from 49 clinical trials and observational studies.

The results of the genomic analysis and validation experiments on independent datasets showed the algorithm was 95 percent accurate in assessing vaccine effectiveness.

According to Wang, the algorithm does more than allow for the rapid assessment of a vaccine’s effectiveness against novel variants. In addition, the information obtained from the VE-GD framework can be used to determine which ingredients for a vaccine would be most effective in protecting against the prevailing virus strains.

Wang said this new technology facilitates vaccine design, but also provides new ideas for vaccine development and evaluation for other viruses, including influenza.

Zee claims the new algorithm is particularly important for public health since it provides a real-time prediction of the effectiveness of new vaccines before their widespread use to prevent infections.

Zee suggests this new technology can help vaccine manufacturers select candidate vaccine antigens and design clinical trials. Healthcare professionals and policymakers can also use the technology to estimate the size of outbreaks caused by mutated viruses based on their predicted vaccine efficacy information.

Prior to the introduction of CUHK’s algorithm, a vaccine’s effectiveness would only be measured after it was administered, and people had become infected. Now when a newly mutated virus emerges, scientists can use the algorithm to assess a vaccine’s potential efficacy before it’s administered, and even determine the most suitable ingredients for its manufacture.

CUHK’s algorithm has innovated the use of sequencing data to provide real-time evaluation of a vaccine’s effectiveness against mutated viruses. The algorithm can also assist in the design of better vaccines and can be applied to clinical trial planning and pre-application evaluations.