Incorporating Genomic Sequences into Stochastic Transmission Modeling to Improve the Forecasting of the Spread of SARS-CoV-2

Music City Center 

The recent SARS-CoV-2 pandemic has highlighted the growing importance of infectious disease forecasting. An accurate and robust predictive model can empower public health leaders to make timely decisions on isolation and vaccination policies, thereby reducing the number of infections and severe cases. However, the emergence of new variants and subvariants can significantly alter the transmissibility and virulence of the pathogen in a short time, making the number of infections and hospitalizations difficult to predict. To enhance the timeliness and accuracy of forecasting, SARS-CoV-2 sequencing data can be utilized, which is a vast database as millions of sequences have been collected and reported over the past few years. By incorporating the evolution of SARS-CoV-2 virus into classic transmission models, we conclude that genomic data is crucial for capturing trends in epidemiological data when new variants and subvariants emerge, leading to the development of a more reliable forecasting model.

Genomic epidemiology

Phylodynamics

Infectious diseases