Computational Epidemiology

Ellen Kuhl's volume blends mathematical epidemiology with computational modeling, physics-based simulation, data science, and machine learning. Its central question is how COVID-19 outbreaks unfold and how they can be controlled.

The book teaches the tools needed to model and simulate nonlinear dynamic systems, using the global pandemic as the running context. It is written for students, educators, and basic and medical researchers in the natural and social sciences, and for readers interested in working with large datasets.

It works both as a textbook for undergraduate and graduate students and as a monograph for researchers and scientists. Its subject fits courses in applied mathematics, biomedical engineering, biostatistics, computer science, data science, epidemiology, health sciences, machine learning, mathematical biology, numerical methods, and probabilistic programming.

The author also offers a personal reflection on how data-driven modeling figured in the COVID-19 pandemic, shaped by a wish to understand the crisis itself.