Genomic data can improve pandemic modelling, SFU researchers say

Simon Fraser researchers are advocating for the inclusion of genomic data into forecasting models to better understand the spread of infectious diseases. The researchers say incorporating this data into forecasting models can inform monitoring, coordination and help determine where resources are needed. In a paper published this week in Nature Microbiology, SFU mathematics researchers Caroline Colijn, Pengyu Liu and Jessica Stockdale note that genomic sequencing technologies have improved to the point where it is possible to consistently sample over time to understand how pathogens mutate and evolve to produce new variants or strains. As the technology has improved, they say it has become more feasible to integrate genomic data from viruses and other pathogens into predictive mathematical models that forecast the spread of an infection. Models could incorporate the pathogen's diversity, the rate of transmission and interventions such as antibiotic treatment of vaccination. -Genomic data can be used to make forecasting more accurate in assessing risks of immune evasion and antimicrobial resistance (resistance to antibiotics or other treatment options) and can help in mitigating those risks,- says Colijn, who holds a Canada 150 Research Chair in Mathematics for Evolution, Infection and Public Health. She also oversees the Canadian Network for Modelling Infectious Disease (CANMOD) and is a Scientific Co-director of the Pacific Institute on Pathogens, Pandemics and Society (PIPPS) based at SFU.