A researcher from the University of Pittsburgh has developed a novel method for predicting the dominant strain of viral pathogens to allow for more effective vaccine design.
Description
The vaccines for circulating viral pathogens like SARS-CoV-2 and influenza require months of planning to determine which strains or variants should be included. However, viruses regularly mutate impacting on vaccine efficacy. The development of an analytical epidemiological model that infers the impacts of mutations on transmission effects can allow for rapid identification of new variants that require further investigation, thereby improving vaccine efficacy.
Applications
• SARS vaccine
• Influenza vaccine
• Vaccines for other viral pathogens
Advantages
Current approaches to developing the influenza vaccine require global surveillance and monitoring in addition to extensive lab research and computation models to identify which strains should be included in each annual vaccine. Each year, several months before flu season, WHO agree on the strains to include for optimal efficacy. However, viruses evolve overtime and the strain most dominant when the vaccine formulation is agreed upon may not be the strain circulating when vaccines are delivered to the public. Such an approach can impact on vaccine efficacy leading to reduced protection, increased illness, loss to economies, and death.
This novel approach uses publicly available genomic data to track how variants mutate over time, and what impact these mutations have on transmission. The information allows for faster and more accurate prediction of the viral strains likely to be circulating at the time of vaccine deployment.
Invention Readiness
A mathematical model and computer code has been developed. This model can study both virus evolution and disease spread. Testing of this model on SARS-CoV-2 data has identified multiple mutations exhibiting a strong effect on transmission rate, both within and outside the spike protein and has detected variants with increased transmission. Within a week of Alpha and Delta variants of SARS-CoV-2 having a regional prevalence of 1%, the model inferred a considerable transmission advantage for these variants demonstrating the ability of the model to rapidly identify potential dominant strains before they spread more widely, acting as an early warning system for emerging strains.
IP Status
Copyright
