Mathematical Modelling of Coronavirus spread

The podcast discusses mathematical modeling for understanding and containing infectious diseases, particularly focusing on the coronavirus. It highlights the limitations of using exponential curves for modeling disease spread and emphasizes the importance of capturing the essential dynamics of the infectious disease process. The discussion covers compartmental modeling approaches like SIR, SIS, SEIR, and SEIS frameworks, explaining how these models can be used to evaluate containment strategies and determine optimal vaccination strategies. The podcast also delves into deterministic models using differential equations and touches on stochastic processes and network integration for more realistic descriptions. It further explains the mathematical equations behind the SIS model, including the Bernoulli equation and integrating factor method, to determine the number of infectives over time and introduces the concept of the reproduction number as a threshold for epidemic spread. Lastly, the podcast touches on the SIR model, highlighting the final size equation and the insight that some susceptibles will never get infected, and encourages listeners to estimate reproduction numbers for different countries.