Course 2025-2026 a.y.

The course will cover the following topics:

• Discrete-time deterministic and stochastic models
• Ordinary differential equation models
• Metapopulation models 
• Simulation, sensitivity and sampling parameter sets, including MCMC techniques for model calibration
• Network models: reading adjacency matrices and simulation of Reed-Frost models
• Agent-based models
• AI extensions of epidemic models

At the end of the course student will be able to:

• Develop the ability to scrutinize complex systems using modeling and simulation techniques.
• Utilize acquired skills in population dynamic modeling, agent-based modeling, and network science for health
• Investigate underlying mechanisms in diverse biological and population processes, allowing for a comprehensive exploration of real-world applications.
• Develop the capacity to formulate projections for future scenarios based on alternative assumptions.
• Acquire innovative tools from AI techniques

At the end of the course student will be able to:

• Apply advanced modeling and simulation techniques to address and solve complex health-related challenges
• Utilize acquired skills in population dynamic modeling, agent-based modeling, and network science to analyze and solve real-world interdisciplinary problems
• Integrate modeling and simulation skills into counterfactual scenario analysis and decision making processes
• Formulate informed projections for future scenarios by applying alternative assumptions
• Evaluate the appropriateness of different modeling techniques and adapt these to diverse health-related problems
• Apply soft skills such as effective teamwork, clear communication, and collaborative approach when attending classes
• Demonstrate analytical thinking by recognizing patterns and trends in simulated scenarios, leading to the identification of optimal solutions
• Communicate insights of modeling and simulation outcomes clearly and effectively to diverse stakeholders in a professional context
• Exhibit confidence in the application of modeling and simulation methodologies across public and private sectors, contributing to effective problem-solving and translating theoretical knowledge into practical solutions

• Lectures
• Guest speaker's talks (in class or in distance)
• Individual works / Assignments
• Collaborative Works / Assignments

Classical face-to-face lectures focus on the presentation and the discussion of modelling techniques covered by the course, with a main attention to methodology, theory, and possible applications. To improve the learning experience and motivate the interaction, illustrative case studies and in-class exercises may also be considered.

A series of practical sessions, with the students working on their own laptop, are also provided. These classes, typically (but not always), consist of two main parts:

1. The students are guided in the implementation of Python codes
2. After the guided implementation, students are asked to finish the proposed tasks during class and/or independently at home (individual assignment)

For each practical session, students are provided with a Notebook Jupiter file with instructions on the exercises to be done in class and revised at home. The solution of each practical session is provided after the class.

One assignment is required throughout the course. The assignment aims to develop a comprehensive simulation project, creating and presenting a model-based solution to a complex problem, or conducting a joint analysis of a dataset. This approach aim to foster communication and problem-solving skills.

Assessment is based on:

A written exam (i.e. 50%):

• The exam will be closed book (Respondus) with open and closed questions.  The exam covers material covered in the lectures, practical classes, in the text books and other set of readings provided by the Professors. The exam has to be done in one shot at the end of the course.

An individual project (50%):

• The project can be submitted once only and will remain valid throughout the entire academic year. The mark will consider both the validity, robustness and clarity of the written report as well as the outcome of the individual oral presentation in terms of the idea, method and results of the project.

Slides will be made available by professors in advance of the lectures and will represent the main guiding reference for the preparation of the exam. Programming codes, reading materials and selected chapters of relevant books will be uploaded on the e-learning platform.