The Mathematics and Statistics of Infectious Disease Outbreaks

This article is originally published at http://staff.math.su.se/hoehle/blog/

Abstract:

Slides, R code and video lectures of our 2020 The Mathematics and Statistics of Infectious Disease Outbreaks summer course at Stockholm University are made available to a wider audience.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. The markdown+Rknitr source code of this blog is available under a GNU General Public License (GPL v3) license from github.

Introduction

During the 2020 summer Tom Britton and I gave a course on The Mathematics and Statistics of Infectious Disease Outbreaks at the Department of Mathematics, Stockholm University, Sweden. Pre-requisites for the course were undergraduate knowledge of mathematics (e.g. differential equations, optimization) and statistics (e.g. random variables, distributions, maximum likelihood inference) as well as some programming skills in a language with a data science component (python, R, Julia, matlab, …).

which, e.g., links to the Youtube playlist containing the videos.

Course content

• Introduction to the Course [Tom Britton | Michael Höhle]
• L01: Mathematical Modelling [Part 1 | Part 2]
• L02: Simulation and Fitting of Epidemic Models [Part 1 | Part 2 | Part 3]
• L03: Timing and observations + Endemic models [Part 1 | Part 2]
• L04: Estimating Reproduction Numbers [Part 1 | Part 2]
• L05: Effective Reproduction Number [Part 1 | Part 2]
• L06: Latencies and Delays [Part 1 | Part 2]
• L07: Vaccination, other Preventive Measures and Uncertainties [Part 1 | Part 2]
• L08: Modeling using Networks and other Heterogeneities [Part 1 | Part 2]
• L09: Univariate Outbreak Detection [Part 1 | Part 2]
• L10: Multivariate Outbreak Detection [Part 1 | Part 2]
• L11: COVID-19 (I): Reproduction Number and Herd Immunity [Part 1 | Part 2]
• L12: COVID-19 (II): Digital Contact Tracing [Part 1 | Part 2]

Discussion

We hope the material can be of value for those interested in the field, e.g., new Ph.D. students in epidemic modelling, infectious disease epidemiologists with a like for the quantitative side of matters, and for those who just want to improve their armchair epidemiology skills.

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This article is originally published at http://staff.math.su.se/hoehle/blog/
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