Numerical and data-driven techniques for infectious disease simulation and surveillance

Infectious disease modeling plays a crucial role in understanding the spread and control of outbreaks. This study explores numerical and data-driven techniques for simulating the transmission dynamics of infectious diseases and enhancing surveillance efforts. By leveraging mathematical models and computational approaches, we analyze different methodologies for simulating disease spread, incorporating data integration to improve model accuracy. Techniques such as numerical solutions of differential equations, machine learning-based predictions, and statistical data assimilation are discussed, highlighting their strengths and limitations. The work aims to provide insights into the development of robust models for effective disease surveillance, prediction, and control strategies.

Course schedule

Tuesday 22,   10:30-12:30 (TBC)

Thursday 24,  10:30 - 12:30, Aula G

Friday 25,      10:30 -12:30, Aula C