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Introductory Reading List

With the COVID-19 pandemic impacting every aspect of our lives around the globe, many people are taking an interest in epidemiological modelling for the first time. With this in mind, we have prepared a set of resources to serve as an introduction to the field for people with different levels of interest and prior knowledge.

 

Contents

The main demographic that each section is aimed at is denoted with their listing in the contents and under their

section header.
NTF - New to the field
GI - General interest in the field but do not want to learn details about methodology and best practice.
PM - Practitioners and modellers

General Introduction to Agent-Based Modelling

NTF, GI

 

Agent-based Modelling - Gallagher and Bryson

 
 

This is an encyclopaedia article on agent-based modelling and is, therefore, a very good introduction to the field. It provides historical context for the methodology and explains its usage both conceptually and practically. The Boid model by Craig Reynolds is used as an illustrative example.

 

Comparisons

NTF, GI, PM

 

A Comparison of Agent-Based Models and Equation-Based Models for Infectious Disease Epidemiology - Hunter et al.

This paper uses an ABM and an EBM to study the course of measles outbreaks in early 20th century Irish towns. It explains the methods used in particularly comprehensible plain English and finds that the extra information provided by the agent-based model is worth the extra time needed to set up and run it. This result is common for the small populations being studied.

 

This paper provides a good introduction to both types of modelling with minimal maths and jargon, useful to those looking to learn more about the field of disease modelling from other backgrounds.

Mathematical and computational approaches to epidemic modeling: a comprehensive review - Duan et al.

In this review article, the authors describe three major types of epidemic modelling: mathematical models, complex network models and agent-based models. They explore existing work in each area and compare the key benefits and limitations of each type of model. This paper can serve as a good introduction to epidemic modelling by covering the key ideas in each area of research, including important equations. It should be noted that other authors may consider complex network models to be a subcategory of ABMs.


This is a technical but comprehensive guide to epidemic modelling, especially useful for finding more literature to read or as a comprehensive review of the state of the field in 2015.

 

Using Simulation Results, Statistics and Uncertainty

NTF, GI, PM

Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles - Juul et al.

 

The key lesson from this paper is useful to anyone using scientific data (whether experimental or simulated): No matter how good the data, if the statistical work around it is not done correctly then the conclusions can be spurious. Worries about how people use data have been mentioned in our interviews with academics. Similarly, the need for scientists to give accurate and trustworthy information has been mentioned in our interviews with policymakers. Thus, all practitioners must think deeply and carefully about how they show and describe their data.


The particular contribution of this paper is a set of methods to describe extreme events (e.g. the maximum number of people hospitalised over the course of an epidemic) or likelihoods of particular circumstances (e.g. having some number of people hospitalised each day for several days).

 

Hybrid Models

GI, PM

 

A Hybrid Epidemic Model: Combining the Advantages of Agent-Based and Equation-Based Approaches - Bobashev et al.

This paper creates a hybrid model to simulate the entire process of an epidemic. In the early stages of an epidemic, when small variations can lead to large differences in the outcome, it takes advantage of the ability of ABMs to replicate the details and heterogeneity of the real world. Later, when the epidemic spread has stabilised and simplifying assumption become more accurate, the hybrid model transfers to the less computationally expensive EBMs.
 

Though this hybrid model is useful in its own right, it is worth learning a broader lesson from this paper: All types of modelling have strengths and weaknesses. Therefore, it can be beneficial to use multiple methods and compare the results.