Can epidemiological models predict herd immunity?
Prof. Nico Orce
University of the Western Cape
We have solved the so-called SIR (Susceptible, Infected, Removed) transmission-dynamics equations analytically with (ESIR model) and without (D model) recovery assumptions, characterizing the evolution of pandemic waves at different stages (exponential and slowdown phases, peak, decay, etc). Monte-Carlo simulations based on Planck's quantum model of photons also support these pandemic trends and will also be presented. We applied our models to the different pandemic waves worldwide, where similar trends suggest a common pandemic evolution with universal parameters. Although lockdown conditions continuously change, affecting the initial conditions of the transmission-dynamics equations, our models can be extended to describe additional spatial-time effects arising, for instance, from the release of lockdown measures. Additionally, I'll also show how our results may support herd immunity. Videos from previous seminars and mini-schools can be found @ https://www.youtube.com/c/NicoOrce. Our work has been published in Applied Mathematical Modeling (https://doi.org/10.1016/j.apm.2020.10.019).
Hosted by Prof. Brodeur
All interested persons are invited to attend remotely—email physics@nd.edu for information.