Mathematics has played a prominent role in the pandemic, and mathematical concepts have been in the press throughout, from “flatten the curve” to “herd immunity” to “Rt” and beyond. Mathematical models have also been used by decision-makers to forecast the numbers of infections and cases, project health care demand, and envision longer-term pandemic trajectories. But one of the driving features of the pandemic has been evolution: the virus has changed, increasing its transmissibility, changing its severity (both up and down) and evading our immunity. These changes have posed huge challenges for pandemic control.
In this talk, I will describe the challenges and opportunities that this brings to mathematicians.
The capacity to read the genomes or large numbers of viruses offers a lot of information about how the virus spreads and evolves. But there is a real gap between genetic sequences and interpretable information that can be used to understand dynamics, make projections and support decision-making. I will describe how new mathematical tools help fill this gap, using a combination of discrete structures, estimation and dynamic modelling. Mathematical innovations offer new ways to describe and summarize the information in genetic data, new methods to use those data to learn how pathogens move from person to person and around the world, and new ways to learn where the highest levels of transmission are occurring. I will conclude by outlining ongoing modelling challenges in the world of evolving infectious diseases.
Watch here: https://www.youtube.com/watch?v=gb7_fFPN2gs