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The role of evolution in the
emergence of infectious diseases
Rustom Antia, Roland R. Regoes, Jacob C.
Koella & Carl T. Bergstrom
Nature 2003
Introduction – Novel Diseases
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Disease emergence requires 2 steps:
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Introduction to new host population
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Affected by ecological factors, such as increased
contact between hosts
Growth and spread within new host pop.
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Described by R0
Introduction – Novel Diseases
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R0: “basic reproductive number”
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Average number of new infections per infected
individual, in an otherwise totally susceptible
population.
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R0 < 1: disease dies out
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R0 > 1: disease can spread.
Introduction – Novel Diseases
●
R0
–
Average number of new infections per infected
individual, in an otherwise totally susceptible
population.
–
R0 < 1: disease dies out
–
R0 > 1: disease can spread.
Introduction – Novel Diseases
●
R0: “basic reproductive number”
–
Novel pathogens with R0 > 1 in humans are “an
epidemic waiting to happen”
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Those with R0 < 1 will die out unless R0 is
increased by ecology or evolution.
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Host density or behavior,
Genetic drift in pathogen,
Adaptive evolution by pathogen,
Host genetic changes (less likely).
Scope of this paper
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From introduction into new host population, to
possible emergence as epidemic.
Scope of this paper
Scope of this paper
Scope of this paper
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Probability of emergence
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Depends on
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Initial R0 (“un-evolved”, less than 1)
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Epidemic R0 (“evolved”, greater than 1)
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Number of mutational steps from initial to
epidemic, n,
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μ – the mutant transmission rate.
Scope of this paper
R0, introduced
Scope of this paper
R0, introduced
R0, evolved
Scope of this paper
R0, introduced
μ, mutant trans. rate
R0, evolved
Results
Results
Single step model
Results
Single step model
Prob. that pathogen
evolves to R0 > 1 depends
on mutation rate and
initial R0
Results
Single step model
Results
Single step model
Evolved R0 = 1000
1.5
1.2
Results
Single step model
Evolved R0 = 1000
1.5
1.2
Prob. that pathogen
emerges depends on
mutation rate, initial R0,
and less on evolved R0
Results
Multiple step model
Results
Multiple step model
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Multiple mutations needed to bring R0 > 1
Results
Multiple step model
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Multiple mutations needed to bring R0 > 1
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Jackpot model:
R0 = 2
R0 = 0.5
R0 = 0.5
n = number of steps
R0 = 0.5
R0 = 0.5
Results
Multiple step model
Results
Multiple step model
Highly sensitive to n,
especially at low R0
Results
Multiple step model
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Multiple mutations needed to bring R0 > 1
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Additive model:
R0 = 1
R0 = 0.75
R0 = 0.5
Results
Multiple step model
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Multiple mutations needed to bring R0 > 1
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Fitness Valley model:
Results
Multiple step model
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Multiple mutations needed to bring R0 > 1
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Fitness Valley model:
R0 = 1
R0 = 0.5
R0 = 0.2
Results
Multiple step model
Results
Multiple step model
Additive
Jackpot
Valley
Results
Multiple step model
Additive
Jackpot
Valley
Depends on
- Initial R0,
- mutation rate,
- evolutionary model
Conclusions
Conclusions
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Disease where R0 < 1 may nevertheless
emerge.
Probability of emergence is sensitive to
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Initial and evolved R0,
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Evolutionary rate,
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Type of evolution (jackpot, additive, etc)
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“mutational distance” from R0<1 to R0>1
Conclusions
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Slight increases in R0, via ecology or
behaviour, increase the length of transmission
chains and thereby increase probability of
emergence.
Conclusions
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Slight increases in R0, via ecology or
behaviour, increase the length of transmission
chains and thereby increase probability of
emergence.