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Transcript of virulence evolution (IGERT symposium)
Overview Emerging disease Seasonal disease Theory vs. data References
Eco-evolutionary virulence of pathogens:models and speculations
Ben Bolker, McMaster UniversityDepartments of Mathematics & Statistics and Biology
IGERT symposium
25 April 2014
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Acknowledgements
People Arjun Nanda and Dharmini Shah; Christophe Fraser;Marm Kilpatrick; Anson Wong
Support NSF IRCEB grant 9977063; QSE3 IGERT; NSERCDiscovery grant
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Host-pathogen evolutionary biology
Why is it interesting?
Intellectual merit
Coevolutionary loopsCryptic e�ectsEco-evolutionary dynamics (Luo and Koelle, 2013)Cool storiesLots of data (sometimes)
Broader applications
MedicalConservation and managementOutreach
Overview Emerging disease Seasonal disease Theory vs. data References
Host-pathogen evolutionary biology
Why is it interesting?
Intellectual merit
Coevolutionary loopsCryptic e�ectsEco-evolutionary dynamics (Luo and Koelle, 2013)Cool storiesLots of data (sometimes)
Broader applications
MedicalConservation and managementOutreach
Overview Emerging disease Seasonal disease Theory vs. data References
Virulence: de�nitions
General public: badness
Plant biologists: infectivity
Evolutionists: loss of host �tness
Theoreticians: rate of host mortality(mortality rate vs. case mortality vs. clearance)
Overview Emerging disease Seasonal disease Theory vs. data References
Evolution of virulence evolution theory
Classical dogma monotonic trend toward avirulence
Ewald era virulence as an evolved (adaptive) trait. Tradeo�theory, modes of transmission.
post-Ewald more formal tradeo� models, mostly based on R0
optimization. Adaptive dynamics
Now tradeo� backlashwithin-host dynamics/multi-level modelseco-evolutionary dynamicshost e�ects: resistance vs tolerance vs virulence
Overview Emerging disease Seasonal disease Theory vs. data References
Evolution of virulence evolution theory
Classical dogma monotonic trend toward avirulence
Ewald era virulence as an evolved (adaptive) trait. Tradeo�theory, modes of transmission.
post-Ewald more formal tradeo� models, mostly based on R0
optimization. Adaptive dynamics
Now tradeo� backlashwithin-host dynamics/multi-level modelseco-evolutionary dynamicshost e�ects: resistance vs tolerance vs virulence
Overview Emerging disease Seasonal disease Theory vs. data References
Evolution of virulence evolution theory
Classical dogma monotonic trend toward avirulence
Ewald era virulence as an evolved (adaptive) trait. Tradeo�theory, modes of transmission.
post-Ewald more formal tradeo� models, mostly based on R0
optimization. Adaptive dynamics
Now tradeo� backlashwithin-host dynamics/multi-level modelseco-evolutionary dynamicshost e�ects: resistance vs tolerance vs virulence
Overview Emerging disease Seasonal disease Theory vs. data References
Evolution of virulence evolution theory
Classical dogma monotonic trend toward avirulence
Ewald era virulence as an evolved (adaptive) trait. Tradeo�theory, modes of transmission.
post-Ewald more formal tradeo� models, mostly based on R0
optimization. Adaptive dynamics
Now tradeo� backlashwithin-host dynamics/multi-level modelseco-evolutionary dynamicshost e�ects: resistance vs tolerance vs virulence
Overview Emerging disease Seasonal disease Theory vs. data References
Basic tradeo� theory: assumptions
Homogeneous, non-evolving hosts
No superinfection/coinfection
Horizontal, direct transmission
Tradeo� between rate of transmissionand length of infectious period
Infectious period ∝ 1/clearance(= recovery+disease-induced mortality+natural mortality)
Overview Emerging disease Seasonal disease Theory vs. data References
Tradeo�s, R0, and r
Clearance+disease−induced mort.
Transmissionrate
mu 0 1 2 3 4 5
Overview Emerging disease Seasonal disease Theory vs. data References
Tradeo�s, R0, and r
Clearance+disease−induced mort.
Transmissionrate
mu 0 1 2 3 4 5
Overview Emerging disease Seasonal disease Theory vs. data References
Tradeo�s, R0, and r
Clearance+disease−induced mort.
Transmissionrate
mu 0 1 2 3 4 5
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Epidemiological model
SIR model
Constant population size(birth=death)
Ignore recovery
Rescale: µ = 1, N = 1(time units of host lifespan)
I
S
disease−
mortality(α)induced
mortality(µ)
birth
R
infection (β)
recovery
Overview Emerging disease Seasonal disease Theory vs. data References
Epidemiological model
SIR model
Constant population size(birth=death)
Ignore recovery
Rescale: µ = 1, N = 1(time units of host lifespan)
I
S
disease−
mortality(α)induced
mortality(µ)
birth
R
infection (β)
recovery
Overview Emerging disease Seasonal disease Theory vs. data References
Epidemiological model
SIR model
Constant population size(birth=death)
Ignore recovery
Rescale: µ = 1, N = 1(time units of host lifespan)
I
S
disease−
mortality(α)induced
mortality(µ)
birth
R
infection (β)
recovery
Overview Emerging disease Seasonal disease Theory vs. data References
Epidemiological model
SIR model
Constant population size(birth=death)
Ignore recovery
Rescale: µ = 1, N = 1(time units of host lifespan)
I
S
disease−
mortality(α)induced
mortality(µ)
birth
R
infection (β)
recovery
Overview Emerging disease Seasonal disease Theory vs. data References
The model (2): evolutionary dynamics
Incorporate trait dynamics
Standard quantitative genetics model (Abrams, 2001):
Fitness depends on mean trait value (α)and ecological context (proportion susceptible)
Constant additive genetic variance Vg
Trait evolves toward increased �tness:rate proportional to ∆�tness/∆trait
Alternatives:multi-strain, adaptive dynamics, PDEs, agent-based models . . .
Overview Emerging disease Seasonal disease Theory vs. data References
The model (2): evolutionary dynamics
Incorporate trait dynamics
Standard quantitative genetics model (Abrams, 2001):
Fitness depends on mean trait value (α)and ecological context (proportion susceptible)
Constant additive genetic variance Vg
Trait evolves toward increased �tness:rate proportional to ∆�tness/∆trait
Alternatives:multi-strain, adaptive dynamics, PDEs, agent-based models . . .
Overview Emerging disease Seasonal disease Theory vs. data References
The model (2): evolutionary dynamics
Incorporate trait dynamics
Standard quantitative genetics model (Abrams, 2001):
Fitness depends on mean trait value (α)and ecological context (proportion susceptible)
Constant additive genetic variance Vg
Trait evolves toward increased �tness:rate proportional to ∆�tness/∆trait
Alternatives:multi-strain, adaptive dynamics, PDEs, agent-based models . . .
Overview Emerging disease Seasonal disease Theory vs. data References
The model (2): evolutionary dynamics
Incorporate trait dynamics
Standard quantitative genetics model (Abrams, 2001):
Fitness depends on mean trait value (α)and ecological context (proportion susceptible)
Constant additive genetic variance Vg
Trait evolves toward increased �tness:rate proportional to ∆�tness/∆trait
Alternatives:multi-strain, adaptive dynamics, PDEs, agent-based models . . .
Overview Emerging disease Seasonal disease Theory vs. data References
The model (2): evolutionary dynamics
Incorporate trait dynamics
Standard quantitative genetics model (Abrams, 2001):
Fitness depends on mean trait value (α)and ecological context (proportion susceptible)
Constant additive genetic variance Vg
Trait evolves toward increased �tness:rate proportional to ∆�tness/∆trait
Alternatives:multi-strain, adaptive dynamics, PDEs, agent-based models . . .
Overview Emerging disease Seasonal disease Theory vs. data References
Evolutionary dynamics, cont.
Virulence
Fitn
ess
(w) frac inf=0.1
Overview Emerging disease Seasonal disease Theory vs. data References
Evolutionary dynamics, cont.
Virulence
Fitn
ess
(w) frac inf=0.1
frac inf=0.3
Overview Emerging disease Seasonal disease Theory vs. data References
Power-law tradeo� curves
Virulence
Tran
smis
sion
β(α) = cα1 γ
c = 2, γ = 2
c = 1, γ = 2
c = 1, γ = 3
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
(Why) are emerging pathogens more virulent?
What might explain initially high, but rapidly decreasing, virulenceof emerging pathogens?
Pathogens with low virulence go unnoticed
Hosts less resistant to / tolerant of novel parasites
High transmission → frequent coinfection → selection forvirulence
Disease-induced drop in population density decreases selectionfor virulence (Lenski and May, 1994)
Overview Emerging disease Seasonal disease Theory vs. data References
(Why) are emerging pathogens more virulent?
What might explain initially high, but rapidly decreasing, virulenceof emerging pathogens?
Pathogens with low virulence go unnoticed
Hosts less resistant to / tolerant of novel parasites
High transmission → frequent coinfection → selection forvirulence
Disease-induced drop in population density decreases selectionfor virulence (Lenski and May, 1994)
Overview Emerging disease Seasonal disease Theory vs. data References
(Why) are emerging pathogens more virulent?
What might explain initially high, but rapidly decreasing, virulenceof emerging pathogens?
Pathogens with low virulence go unnoticed
Hosts less resistant to / tolerant of novel parasites
High transmission → frequent coinfection → selection forvirulence
Disease-induced drop in population density decreases selectionfor virulence (Lenski and May, 1994)
Overview Emerging disease Seasonal disease Theory vs. data References
(Why) are emerging pathogens more virulent?
What might explain initially high, but rapidly decreasing, virulenceof emerging pathogens?
Pathogens with low virulence go unnoticed
Hosts less resistant to / tolerant of novel parasites
High transmission → frequent coinfection → selection forvirulence
Disease-induced drop in population density decreases selectionfor virulence (Lenski and May, 1994)
Overview Emerging disease Seasonal disease Theory vs. data References
Transient virulence
Selection di�ers between the epidemic and endemic phases of anoutbreak (Frank, 1996; Day and Proulx, 2004)
endemic phase selection for per-generation o�spring production:maximize R0, βN/(α + µ)
epidemic phase selection for per-unit-time o�spring production:maximize r , βN − (α + µ)
Overview Emerging disease Seasonal disease Theory vs. data References
Transient virulence
Selection di�ers between the epidemic and endemic phases of anoutbreak (Frank, 1996; Day and Proulx, 2004)
endemic phase selection for per-generation o�spring production:maximize R0, βN/(α + µ)
epidemic phase selection for per-unit-time o�spring production:maximize r , βN − (α + µ)
Overview Emerging disease Seasonal disease Theory vs. data References
Transient virulence
Selection di�ers between the epidemic and endemic phases of anoutbreak (Frank, 1996; Day and Proulx, 2004)
endemic phase selection for per-generation o�spring production:maximize R0, βN/(α + µ)
epidemic phase selection for per-unit-time o�spring production:maximize r , βN − (α + µ)
Overview Emerging disease Seasonal disease Theory vs. data References
Transient emerging virulence
When a parasite previously in eco-evolutionary equilibriumemerges in a new host population (at low density) it will showa transient peak in virulence as it spreads
How big is the peak? Does it matter?
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Model parameters
Parameter
c Transmissionscale
γ Transmissioncurvature
I (0) Initialepidemic size
Vg Genetic variance
Alternative
R∗0 Equilibrium R0
α∗ Equilibriumvirulence
1/N0 Inversepopulation size
Overview Emerging disease Seasonal disease Theory vs. data References
Example
Time
Fra
ctio
n in
fect
ive
0.00
0.05
0.10
0.15
0 10 20 30
Vg = 5, c = 3, I(0) = 0.001, γ = 2
(R0* = 1.5, α* = 1, N = 1000)
1.01.21.41.61.82.0
α
Overview Emerging disease Seasonal disease Theory vs. data References
Response variables
Time
peaktime
peak height(α)
Overview Emerging disease Seasonal disease Theory vs. data References
Peak height
Equilibrium transmission (R0*)
Equ
ilibr
ium
viru
lenc
e (α
* )
1
10
100
1000
1.1 2 5 10 50
1.025
I(0) = 10−2
CV
g=
0.1
1.0251.05
I(0) = 10−3
CV
g=
0.1
1.1 2 5 10 50
1.05
1.075
I(0) = 10−4
CV
g=
0.1
1.5
I(0) = 10−2
CV
g=
0.5
1.5
2.0
I(0) = 10−3
CV
g=
0.5
1
10
100
1000
1.5
2.0 I(0) = 10−4
CV
g=
0.51
10
100
1000
1.5
2.02.5
3.0
I(0) = 10−2 C
Vg
=1
1.1 2 5 10 50
1.52.0
2.5
3.0
3.5
I(0) = 10−3
CV
g=
1
1.52.02.5
3.03.5
4.0
I(0) = 10−4
CV
g=
11.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Overview Emerging disease Seasonal disease Theory vs. data References
Estimates for emerging pathogens
Order of magnitude estimates for some emerging high-virulencepathogens:
Pathogen R∗0
α∗ Reference
SARS 3 640 Anderson et al. (2004)HIV 1.43 6.36 Velasco-Hernandez et al. (2002)
West Nile 1.61�3.24 639 Wonham et al. (2004)myxomatosis 3 5 Dwyer et al. (1990)
Overview Emerging disease Seasonal disease Theory vs. data References
Emerging pathogens: where are we?
CVg = 0.5, I (0) = 10−3 (middle panel):
R0
Equ
ilibr
ium
viru
lenc
e (α
* )
1
10
100
1000
1.1 2 5 10 50
1.5
2.0
SARS
HIV
WNV
MYXO
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Overview
Mosquito-borne viral disease of rabbits
Benign in South American rabbits,quickly fatal in European rabbits
Well characterized (Fenner et al., 1956; Dwyer et al., 1990)
Overview Emerging disease Seasonal disease Theory vs. data References
Myxomatosis tradeo� curve
Scaled virulence
Tota
l tra
nsm
issi
on
0 2 4 6 8 10 12
0.0
0.2
0.4
0.6
eq epi
Overview Emerging disease Seasonal disease Theory vs. data References
Estimating evolvability (Vg)
Key parameter: genetic variance in virulence (evolvability)
Despite case studies of rapid pathogen evolution:
myxomatosis (Dwyer et al., 1990)syphilis (Knell, 2004)serial passage experiments (Ebert, 1998)Plasmodium chabaudi (Mackinnon and Read, 1999a)
we rarely have enough information to estimate Vg
Only (?) for myxomatosis do we know the variation invirulence among circulating strains
Overview Emerging disease Seasonal disease Theory vs. data References
Estimating evolvability (Vg)
Key parameter: genetic variance in virulence (evolvability)
Despite case studies of rapid pathogen evolution:
myxomatosis (Dwyer et al., 1990)syphilis (Knell, 2004)serial passage experiments (Ebert, 1998)Plasmodium chabaudi (Mackinnon and Read, 1999a)
we rarely have enough information to estimate Vg
Only (?) for myxomatosis do we know the variation invirulence among circulating strains
Overview Emerging disease Seasonal disease Theory vs. data References
Estimating evolvability (Vg)
Key parameter: genetic variance in virulence (evolvability)
Despite case studies of rapid pathogen evolution:
myxomatosis (Dwyer et al., 1990)syphilis (Knell, 2004)serial passage experiments (Ebert, 1998)Plasmodium chabaudi (Mackinnon and Read, 1999a)
we rarely have enough information to estimate Vg
Only (?) for myxomatosis do we know the variation invirulence among circulating strains
Overview Emerging disease Seasonal disease Theory vs. data References
Myxomatosis grades vs. time
1950 1954 1956 1961 1965 1968 1972 1978
Proportion
0.0
0.2
0.4
0.6
0.8
1.0Virulence grade
I II III IV V
Overview Emerging disease Seasonal disease Theory vs. data References
Myxomatosis variance vs. time
Date
Gen
etic
var
ianc
e (V
g)
0
10
20
30
40
1950 1960 1970
Vg= 10Vg= 2.5
Vg= 40
Overview Emerging disease Seasonal disease Theory vs. data References
Myxomatosis virulence dynamics: power-law tradeo�
Date
Sca
led
viru
lenc
e
0
5
10
15
20
25
1950 1960 1970
h=2.5
h=10
h=40
Overview Emerging disease Seasonal disease Theory vs. data References
Myxomatosis virulence dynamics: realistic tradeo�
Date
Sca
led
viru
lenc
e
0
5
10
15
20
25
1950 1960 1970
h=40
h=10h=2.5
Overview Emerging disease Seasonal disease Theory vs. data References
Myxo virulence: equilibrium start, power-law tradeo�
Date
Sca
led
viru
lenc
e
0
5
10
15
1950 1955
h=40h=10h=2.5
Overview Emerging disease Seasonal disease Theory vs. data References
Myxo virulence: equilibrium start, realistic tradeo�
Date
Sca
led
viru
lenc
e
0
5
10
15
1950 1955
h=40h=10h=2.5
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Seasonality
Many pathogens �uctuate annually
Host contact/aggregation patternsHost (or vector) demographyClimatic e�ects on transmissibility
Fluctuating incidence = �uctuating selection
Seasonal variation or latitudinal variation?
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Toy model
Basic Ross-MacDonaldvector-host model
Simple vector (mosquito)demography
No host demography
Two pathogen strains
I disease−
mortality(α)induced
S
I
infection (β)
recovery
S
R
host vector
Overview Emerging disease Seasonal disease Theory vs. data References
Case I: r1 > r2, equal R0
0.000
0.025
0.050
0.075
0 25 50 75 100 125time
dens
ity
variable
I1
I2
0.0
0.2
0.4
0 25 50 75 100 125time
frac
tion
of s
trai
n 1
Overview Emerging disease Seasonal disease Theory vs. data References
Case II: R0,1 > R0,2, equal r
0.00
0.05
0.10
0.15
0.20
0 25 50 75 100 125time
dens
ity
variable
I1
I2
0.5
0.6
0.7
0.8
0.9
1.0
0 25 50 75 100 125time
frac
tion
of s
trai
n 1
Overview Emerging disease Seasonal disease Theory vs. data References
Case III: R0,1 > R0,2, r2 > r1
0.00
0.05
0.10
0.15
0 25 50 75 100 125time
dens
ity
variable
I1
I2
0.5
0.6
0.7
0.8
0.9
1.0
0 25 50 75 100 125time
frac
tion
of s
trai
n 1
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Titer vs infectiousness
● ● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
0.0
0.2
0.4
0.6
4 6 8titer
Tran
smis
sion
pro
babi
lity
source
●
●
●
●
Dohm
Tiawsirisup_2005_VBZD
Turell_altjmh
Turell_JME
Overview Emerging disease Seasonal disease Theory vs. data References
Titer curves (American crows)
1e−04
1e−02
1e+00
2 4 6 8day
tran
smis
sion
pro
babi
lity
strain
BIRD1153
KEN
KENsub
NY99
P991
P991sub
TM171−03−pp5
TM173−03−pp1
TWN301
Overview Emerging disease Seasonal disease Theory vs. data References
Transmission vs clearance for WNV
BIRD1153BIRD1461
NY99
TM171−03−pp5BIRD1153KEN
KENsub
NY99P991
TM171−03−pp5
TWN301
0.0
0.2
0.4
0.6
0.00 0.25 0.50 0.75 1.00Clearance rate (1/infectious period)
Ave
rage
tran
smis
sion
rat
e
species
aa
sparrow
amcrow
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Estimating tradeo� curves
Usually assume a tradeo� between virulence and transmission
Positive correlation virulence and transmissibility (or proxies)known from many systems (Lipsitch and Moxon, 1997)
the shape of tradeo� curves is largely unknown
Overview Emerging disease Seasonal disease Theory vs. data References
Malaria (Mackinnon and Read, 1999b; Paul et al., 2004)
●
●●●
0 500 1000 1500
0
20
40
60
80
100
Scaled virulence
% m
osqu
itoes
infe
cted
Plasmodium gallinaceum
●
low−dose mixedhigh−dose mixedSLThai
●
●
●
●
●●
●
●
10 15 20 25
10
15
20
25
Maximum parasitemia
Ove
rall
infe
ctio
n (%
)
Plasmodium chabaudi
Overview Emerging disease Seasonal disease Theory vs. data References
Pasteuria ramosa (Jensen et al., 2006)
●●●●●
●
●
●
●●
●
●
●
●●●
●●
●
●
●
●
●
●●●●
●
●●
●
0 1 2 3 4 50.00
0.02
0.04
0.06
0.08
0.10
0.12
Scaled virulence
Spo
res/
day
(×10
6 )
●●●●●
●
●
●
●●
●
●
●
●●●
●●
●
●
●
●
●
●●●●
●
●●
●
Overview Emerging disease Seasonal disease Theory vs. data References
HIV (Fraser et al., 2007)
0 10 20 30 40 50 60
0.0
0.1
0.2
0.3
0.4
0.5
Scaled virulence
Tran
smis
sion
rat
e
eq epi
Overview Emerging disease Seasonal disease Theory vs. data References
HIV dynamics (Shirre� et al., 2011)
Overview Emerging disease Seasonal disease Theory vs. data References
Phage dynamics (Berngruber et al., 2013)
Overview Emerging disease Seasonal disease Theory vs. data References
What about space?
Theory: spatial structureshould select for decreasedvirulence
Experiment: viscositydecreases infectivity inPlodia (Boots and Mealor,2007)
Are we ready for space?
Overview Emerging disease Seasonal disease Theory vs. data References
Outline
1 OverviewThe evolution of host-pathogen theoryToy models
2 Transient virulence and emerging diseasesOverviewToy modelMyxomatosis data
3 Transient virulence and seasonalityOverviewToy modelWNV data
4 More on theory vs. dataTradeo� curvesConclusions
Overview Emerging disease Seasonal disease Theory vs. data References
Conclusions
Eco-evolutionary dynamics of virulence are still plausible(Alizon et al., 2009; Luo and Koelle, 2013)
Sensitive to genetic variance and shape of tradeo� curve
Theory meets molecular biology:mutations of large e�ect vs. quantitative variability
Overview Emerging disease Seasonal disease Theory vs. data References
Conclusions
Eco-evolutionary dynamics of virulence are still plausible(Alizon et al., 2009; Luo and Koelle, 2013)
Sensitive to genetic variance and shape of tradeo� curve
Theory meets molecular biology:mutations of large e�ect vs. quantitative variability
Overview Emerging disease Seasonal disease Theory vs. data References
Crome (1997) on theory
When we regard theories as tight, real entities and devote
ourselves to their analysis, we can limit our horizons and,
worse, attempt to make the world �t them. A lot of
ecological discussion is not about nature, but about
theories, generalizations, or models supposed to represent
nature . . .
Overview Emerging disease Seasonal disease Theory vs. data References
References
Abrams, P.A., 2001. Ecol Lett, 4:166�175.
Alizon, S., Hurford, A., et al., 2009. J. Evol. Biol., 22:245�259.doi:10.1111/j.1420-9101.2008.01658.x.
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