Epidemiology modeling with Stella
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Transcript Epidemiology modeling with Stella
Epidemiology modeling
with Stella
CSCI 1210
Stochastic vs. deterministic
Suppose there are 1000
individuals and each one
has a 30% chance of
being infected:
Stochastic method: run
the model on the right
1000 times
Deterministic method:
1000 * 30% = 300 get
infected
(Law of Mass Action)
Stella Stocks and Flows
takes “stuff” out from a stock or
puts stuff into a stock
A flow
Result of simple flow model
Simple Epidemic Flow models
A short-term
illness with recovery and
permanent immunity
Simple Epidemic Flow Models
Short-term
lethal illness with no recovery
or immunity
Examples: “Martian flu”, measles in Incas
Note the flow into a sink outside the model
Simple Epidemic Flow Models
Short-term
illness with recovery and
temporary immunity
Example: malaria
Filling out the model
These
are dynamic models
The value of each stock depends only on
the initial value and the flows over time
The flows depend on the assumptions and
state of the model – this is what
determines how the model works
The Infection process
Simplest
model: small population in which
everyone is in contact
Each sick person has a certain constant
probability of infecting each susceptible
person in one time unit
Size of infection flow depends on the
number of sick people and the number of
susceptibles.
Modeling infection in Stella
The thin arrows represent influences. Note that
all the influences affect the rate of infection.
We leave out incubation for simplicity: everyone
is either susceptible or ill.
Qualitative analysis of infection
When
there are few sick people, there can
be little infection
When nearly everyone is sick, there can
be little infection
Maximum infection will occur when the
population is between these cases
Eventually, everyone will get sick.
Results of simple SI model
Results of simple SI model
A model with recovery and
immunity
After recovery, people are neither susceptible
nor ill
A certain fraction of ill people will recover
each time period.
The rate of recoveries depends on the
number of ill people.
Results of the SIS model
Infection and recovery rates
Effect of immunization
Reduces
the initial number of susceptibles
This reduces the infection rate, but does
not alter the recovery rate
If the infection rate is small enough, the
disease will die out without becoming an
epidemic (herd immunity).
Infection and recovery, with herd
immunity
Results of immunization
campaign
Notes on Herd immunity
Not
necessary to vaccinate the entire
population.
Even individuals who were not vaccinated
share the benefits.
HIV
Human
Immunodiciency Virus (HIV)
A retrovirus
Originated in Africa, probably in 20th
century
Descended from simian virus (SIV) which
“jumped hosts”
Long, contagious incubation period
From HIV to AIDS
Virus
attacks human immune system
Death is from opportunistic secondary
infections, not HIV itself
Anti-retroviral drugs can slow the virus and
prolong life.
AIDS and Africa
42
million HIV/AIDS cases worldwide
29 million cases in Africa
Origin of the virus
Anarchy in central Africa (Uganda,
Rwanda, Congo) helps spread the disease
AIDS: the “Gay Plague”?
Initially,
US AIDS cases were almost all in
gay men
However, African AIDS cases are mostly
heterosexual
More US heterosexual AIDS cases as time
has passed
What gives?
A two-tier model
High-risk
group initially contracts the
disease
Low-risk group does not have the disease
Slight interaction between groups
Two submodels proceed separately but
have a weak coupling
Two-tier model
Results of the two-tier model
AIDS and the “Martian Flu”
HIV/AIDS
is incurable, fatal, and has no
known immunity
However, US AIDS epidemic may have
peaked
So, “Martian Flu” model needs elaboration
Elaborated AIDS model
Add
birth and death flows for susceptibles
who do not get infected
Either die naturally, retire from sex, or
enter monogamous relationships
Creates a situation similar to “herd
immunity” model
Elaborated single-pool model
AIDS model with high riskiness
AIDS model with low riskiness