1
Introduction to infectious disease modelling
Jamie LloydSmith
Epi 220, Fall 2010
UCLA
• Why model?
• Some terminology
• Microparasite models (the SIR family)
• Modeling transmission
•
R
0
• Macroparasite models
Basic results from SIR models
Outline
•
• Elements of more advanced models
• Types of data that modelers can use
• Linking infectious diseases to virology, evolution, ecology,
and beyond
• Convince you that models are useful and relevant.
• Teach enough that you can be an intelligent consumer of
modeling studies.
• Lay groundwork for you to collaborate with modelers in future
– or even do it yourself!
Goals
Why do we model infectious diseases?
1. Gain insight into
mechanisms
influencing disease spread, and link
individual scale ‘clinical’ knowledge with populationscale patterns.
2. Focus thinking
: model formulation forces clear statement of
assumptions, hypotheses.
3. Derive
new insights and hypotheses
from mathematical analysis or
simulation.
4
Establish
relative importance
of different processes and parameters
Following Heesterbeek & Roberts (1995)
4. Establish
of different processes and parameters,
to focus research or management effort.
5. Thought experiments
and “what if” questions, since real experiments
are often logistically or ethically impossible.
6. Explore
management options
.
Note the absence of
prediction.
Models are highly simplified
representations of very complex systems, and parameter values are
difficult to estimate.
exact quantitative predictions are virtually impossible.
Why do we model?
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document2
Epidemic models: the role of data
Why work with data?
Basic aim is to describe real patterns, solve real problems.
Test assumptions!
Get more attention for your work
jobs, fame, fortune, etc
influence public health policy
Challenges of working with data
Hard to get good data sets.
The real world is messy!
And sometimes hard to understand.
Statistical methods for nonlinear models can be complicated.
What about pure theory?
Valuable for clarifying concepts, developing methods, integrating ideas.
(My opinion) The world needs a few brilliant theorists, and
many
strong
applied modellers.
Modeling terminology
State variables
:
quantities that describe the entities of
interest for your model.
e.g. population size, allele frequency, disease
prevalence
Parameters
:
quantities that govern the dynamics, but don’t
describe the state of the system and (typically) don’t
change over time.
e.g. per capita birth rate, carrying capacity, mean
lifespan
Infectious disease epidemiology
Incidence: number of new infections per unit time.
Prevalence: proportion of population that is infected at a
particular time.
Attack rate:
proportion of susceptible individuals in a given
setting that become infected.
This is the end of the preview.
Sign up
to
access the rest of the document.
 Fall '10
 A
 Epidemiology, population size

Click to edit the document details