Computational methods in Epidemiology
- Unit Coordinator: Raffaele D'Ambrosio
- Programme: Erasmus Mundus
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: University of L'Aquila
- Language: English
- Aims:
Students will learn how to implement and apply the mathematical and statistical models and methods that at the moment are most successfully applied in epidemiology. Both spatial and temporal aspects are considered. The main, but not unique, framework is the stochastic one.
- Content:
- Informative and quantitative descriptive models, e.g. compartment models, random graphs, ETAS model. Spatiotemporal noninformative models, e.g. autoregressive models, nonparametric models. Intervention measures models.
- Bayesian statistics models. Reproductive number Bayesian estimation.
- Statistical inference.
- Stochastic simulation and validation. Monte Carlo Simulation. Stochastic algorithms. Stochastic simulation with MatLab. Interpolation. Prediction. Trends. Clustering. Features extraction. Hypothesis testing.
- Pre-requisites:
Mathematical analysis, linear algebra, probability theory, scientific programming.
- Reading list:
- Mathematical Biology - I. An Introduction | James D. Murray | Springer
- Lectures on Monte Carlo Methods. Neal Madras. Fields Institute Monograph
- Markov Chain Monte Carlo in Practice | David Spiegelhalter, Sylvia Richardson, W. R. Gilks. Taylor & Francis Group
- Selected recent papers in scientific literature
