Course manual 2023/2024

Course content

Introduction to Computational Science.

Computational science (sometimes called scientific computing) is an interdisciplinary field that uses advanced computing technologies, mathematical modeling, numerical simulation, and data analysis to study and solve complex problems across various domains of science and engineering. It combines knowledge and techniques from mathematics, computer science, and scientific disciplines (such as physics, chemistry, biology, engineering, and more) to tackle issues that are too large, too small, too complex, or too dangerous to study by traditional experimental or theoretical means

The course aims to give an overview of different modelling approaches using a single application domain:

Modelling Infectious Diseases

This is not a course on epidemiology! But you will get enough information about how to model epidemics so that it is useful.

3 Primary approaches are covered:

Ordinary Differential Equations (~60%)

How to write them, analyse them, simulate them (all briefly)

Complex Networks (~25%)

Types of networks, how to analyse them, model with them, etc.

Agent-based Models (~15%)

Brief Introduction, Examples from literature.

Study materials

Literature

"Modeling Infectious Diseases in Humans and Animals" by Matt Keeling and Pejman Rohani.
Nonlinear Dynamics and Chaos With Applications to Physics, Biology, Chemistry, and EngineeringBySteven H. Strogatz
Network ScienceBook by Albert-László Barabási

Software

Mathematica
Python (Scipy, numpy)
NetLogo

Other

Lecture notes, hand outs, on-line material.

Objectives

The aim of this course is to provide an overview of Computational Science and modelling techniques available to the Computational Scientist
The student will understand and be able to describe and apply the basic concepts of modelling and simulation (Validation, Verification, Experimental Frame, etc.)
The student will be able to design and analyse ODEs as an approach to understand and model real world systems (particularly epidemics)
The student will be able to apply methods of network science to model real world phenomena.
The student will be able to compare modelling approaches and understand under which conditions each approach is most appropriate.
The student will build a basic understanding of epidemics and be able to understand the factors in epidemics that make them challenging to model.
The student will be able to implement and analyse computer simulations using Python (and associated libraries)

Teaching methods

Lecture
Computer lab session/practical training

Lectures, guided individual study paths (depending on prior knowledge), project.

Learning activities

Activity	Number of hours
Lectures	28
Computer Practical	28
Self study	112

Attendance

This programme does not have requirements concerning attendance (Ter part B).

Assessment

Item and weight	Details
Final grade
0.25 (25%) Assignment 1 - SIR model (ODE)	Mandatory
0.25 (25%) Assignment 2 - Stochastic and Spatial Models	Mandatory
0.5 (50%) Exam	Mandatory

There is no minimum for each componenent.

The exam is closed book, hand written and lasts 3 hours.

The resit exam is the same format as the main exam, with similar questions and structure.

The exam resit grade replaces the first exam component, this replaces 50% of the final grade.

Inspection of assessed work

Contact the course coordinator to make an appointment for inspection.

Assignments

Assignment 1: SIR model (ODE)

The SIR model is an epidemiological model that computes the spread of an infectious

disease through a population of people. It is used to compute the fraction of susceptible

(S), infected (I), and recovered (R) individuals at any given time through the spread of an

infectious disease.

Stochastic and Spatial Models

In this assignment you (in teams of two people) will be exploring other ways to model

infectious diseases. In the first part of the assignment you will use a stochastic discrete

event model to compute the spread of an infectious disease through a population. And in

the second half of the assignment you will explore spatial models (in particular networks)

to study the spread of infectious diseases.

Assignment 1

This assignment will focus on the SIR (ODE) epidemiological model. It is an individual assignment. The assignment should be submitted as two separate files. One lab report as a PDF document, and one Jupyter notebook (.ipynb file). Each filename should include your last name, student ID, and the assignment number. For example: M_Lees_123456789_assignment1.pdf and M_Lees_123456789_assignment1.ipynb.

The assignment will be graded following this weighting scheme.

30% -- Quality of the report (introduction, background/theory, experimental method, discussion, references)

55% -- Content (Answers the topics presented in the assignment)

15% -- Code (Does the code reproduce the results presented in the report)

Assignment 2

This is a group assignment, you can turn in your work in groups of 2 people. Make sure both names are included in the report. Please sign up in groups via canvas

This assignment will focus on other modelling techniques for infectious diseases. Specifically, this assignment will focus on Stochastic modelling, meta-population models and Network Models.

The assignment should be submitted as a two separate files. One lab report as a PDF document, and one Python Jupyter notebook (.ipynb file). Each filename should include BOTH STUDENTS last names, student IDs, and the assignment number. For example: Hoekstra_123456789_vangogh_987654321_assignment2.pdf and Hoekstra_123456789_vangogh_987654321_assignment2.ipynb.