Disease emergence in immunocompromised populations

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Disease emergence inimmunocompromised populations

• Jamie Lloyd-Smith
• Penn State University

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Africa a changing immune landscape
HIV prevalence in adult populations
How might this influence disease emergence?
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Heterogeneous immunity and disease emergence
In addition to HIV, many other factors affect the
host immune response to a given pathogen Host
genetics Nutrition Co-infections
Age Immunosuppressive drugs Vaccination and
previous exposure

• Individual-level effects of compromised immunity
  can include
• greater susceptibility to infection higher
  pathogen loads
• disseminated infection and death longer
  duration of infection
• What are the population-level effects of
  immunocompromised groups on pathogen emergence?

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Modelling pathogen emergence
Building on work by Antia et al (2003), Andre
Day (2005), and Yates et al (2006).
Emergence introduction adaptation
invasion
A simple model for pathogen invasion

• Linearized birth-and-death process in continuous
  time.

• Population is structured into groups according to
  immunocompetence.
• Each group has characteristic susceptibility and
  infectiousness, which can vary independently or
  co-vary.

Pathogen is structured into strains representing
stages of adaptation to a novel host species.
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Emergence introduction adaptation
invasion
A simple model for pathogen adaptation
Between-host transmission bottleneck causes
founder effect

• Within-host
• mutation arises during infection and goes to
  fixation within host

Model assumes Occurs with fixed probability per
transmission event.
Occurs at a constant rate within each
infected host.
? a probability over an average duration of
infection.
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Pathogen fitness landscapes
adaptation
One-step adaptation
R0 in healthy population
adaptation
Two-step adaptation
R0 in healthy population
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Model assumptions

• Invasion model (epidemiology)
• Susceptible pool is large compared to outbreak
  size.
• Per capita rates of recovery and transmission are
  constant.
• Type of index case is determined by group size
  weighted by susceptibility
• Pr(index case in group i) (Size of group i)
  (Susc. of group i) .
• Sj (Size of group j) (Susc. of
  group j)
• Adaptation model (evolution)
• Parameters describing relative susceptibility and
  infectiousness dont depend on pathogen strain.
• Evolutionary and epidemiological parameters are
  independent of one another.

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Model equations 1 group, 1 strain
where, because of the large-population assumption
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Model equations 1 group, 2 strains
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Model equations 2 groups, 2 strains
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20 immuno- compromised
80 healthy

• Divide population into two groups, healthy and
  immunocompromised, which mix at random.
• Consider different epidemiological effects of
  immune compromise
• NO EFFECT (0), S?, I? , I?, S?I?, S?I?
• (assume 10-fold changes)
• Infectiousness can vary via either the rate or
  duration of transmission.

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Covariation of epidemiological parameters

• When susceptibility and infectiousness co-vary,
• R0 for the heterogeneous population ? R0 in a
  healthy population.

R0 1 in healthy population
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Pathogen invasion, without evolution
Heterogeneous susceptibility only
1
0
0.8
0.6
Probability of invasion
0.4
0.2
0
0
2
4
6
R
in healthy population
0

• See Becker Marschner, 1990.

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Pathogen invasion, without evolution
Heterogeneous infectiousness only

• See Lloyd-Smith et al, 2005.

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Pathogen invasion co-varying parameters
1
1
S?
, 0
S?
I?
0.8
0.8
0
I?
0.6
0.6
Probability of invasion
Probability of invasion
0.4
0.4
0.2
0.2
0
0
0
2
4
6
0
2
4
6
R
R
in healthy population
in heterogeneous population
0
0

• Solid lines infectiousness varies in
  transmission rate

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Pathogen invasion co-varying parameters

• Dashed lines infectiousness varies in duration

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Pathogen invasion co-varying parameters
Population with heterogeneous infectiousness, I?
Prob. of invasion
R0 when cov(inf, susc) 0
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One-step adaptation
Pathogen evolution probability of adaptation
Pr(between) Pr(within)
w b 110-3 110-3
w gtgt b 110-6 210-3
w ltlt b 210-3 110-6
0
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Pathogen evolution probability of adaptation

• Assuming P(within) P(between) 110-3

S?I?
S?
0
S?
S?I?
I?
S?I?
0
S?I?
I?
Solid lines infectiousness varies in
transmission rate
Dashed lines infectiousness varies in duration
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Pathogen evolution probability of adaptation

• Assuming P(within) P(between) 110-3

S?I?
S?
0
S?
0
0
S?I?
10
10
I?
S?I?
-1
-1
0
10
10
S?I?
I?
-2
-2
10
10
-3
-3
Probability of adaptation
Probability of adaptation
10
10
-4
-4
10
10
-5
-5
10
10
-6
-6
10
10
0
0.5
1
1.5
0
0.5
1
1.5
R
in healthy population

R
in heterogeneous population
0
0
Solid lines infectiousness varies in
transmission rate
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Pathogen evolution probability of adaptation

• Assuming P(within) P(between) 110-3

S?I?
S?
0
S?
0
0
S?I?
10
10
I?
-1
-1
S?I?
0
10
10
S?I?
I?
-2
-2
10
10
Probability of emergence
Probability of emergence
-3
-3
10
10
-4
-4
10
10
-5
-5
10
10
-6
-6
10
10
0
0.5
1
1.5
0
0.5
1
1.5
R
R
in healthy population
in heterogeneous population
0
0
Dashed lines infectiousness varies in duration
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Where does adaptation occur?

• Assuming P(within) P(between) 110-3

1
S?I?
0.8
I?
0.6
Proportion of evolution within host
S?
, 0
0.4
S?I?
0.2
0
0
0.2
0.4
0.6
0.8
1
R

in heterogeneous population
0
Solid lines infectiousness varies in
transmission rate
Dashed lines infectiousness varies in duration
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Where does adaptation occur?

• Assuming P(within) P(between) 110-3

S?I?
I?
S?
, 0
S?I?
Solid lines infectiousness varies in
transmission rate
Dashed lines infectiousness varies in duration
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Two-step adaptation
adaptation
Jackpot model
R0 in healthy population
1
0
Initial strain
Intermediate strain
Adapted strain
Dashed lines 1-step adaptation
Solid lines 2-step adaptation
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Two-step adaptation
adaptation
Jackpot model
R0 in healthy population
1
0
Initial strain
Intermediate strain
Adapted strain
Fitness valley model
R0 in healthy population
1
0
Initial strain
Intermediate strain
Adapted strain
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Two-step adaptation crossing valleys
Pr(between) Pr(within)
w b 110-3 110-3
w gtgt b 110-6 210-3
w ltlt b 210-3 110-6
R0
1
0
Initial strain
Intermediate strain
Adapted strain
-2
within between
10

I?
0
-4
10
-6
Probability of adaptation
10
-8
10
-10
10

-6
-4
-2
0
10
10
10
10
R
of intermediate strain
0
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HIV and acute respiratory infections
Studies from Chris Hari-Baragwanath Hospital in
Soweto.
Bacterial respiratory tract infections (Madhi et
al, 2000, Clin Inf Dis)
Viral respiratory tract infections (Madhi et
al, 2000, J. Ped.)
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HIV and acute respiratory infections
Alagiriswami Cheeseman, 2001
Evans et al, 1995
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Illustration HIV prevalence and influenza
emergence
Assuming Susceptibility is 8? higher in HIV
group, and infections last 3? longer.
P(within) 110-3 Two-step jackpot adaptation
P(between) 110-6 R0 2 for adapted strain
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Summary and future directions

• Invasion
• An immunocompromised group can provide a toe-hold
  for emergence of an unadapted pathogen.
• Positive covariance between susceptibility and
  infectiousness can greatly amplify this effect.
• Adaptation
• Within-host evolution is crucial at low R0, and
  when pathogen must cross fitness valleys to
  adapt.
• Prolonged duration of infection has greater
  influence on emergence than faster rate of
  transmission.
• Next steps
• Link epi and evolution incorporate effect of
  pathogen load?
• Data!! On susceptibility and infectiousness as a
  function of immune status, and on pathogen
  fitness landscapes.
• HIV more data needed at individual and
  population levels

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Acknowledgements

• Ideas and insights
• Bryan Grenfell, Mary Poss, Peter Hudson,
• and many other colleagues at CIDD (Penn State)
• Wayne Getz (UC Berkeley)
• Brian Williams (WHO)
• Sebastian Schreiber
• (UC Davis)
• Funding
• CIDD Fellowship for research
• DIMACS and NSF for travel

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Additional material
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Pathogen evolution approximate calculations

• Can distinguish between mechanisms of evolution
  by considering the total opportunity for each
  to work.
• Total infectious duration L
• Total number of transmission events B
• Andre Day (2005) showed, for a homogeneous
  population, that P(one-step adaptation) m L
  u B
• This argument can be generalized to the
  multi-group setting, using the theory of
  absorbing Markov processes.
• ? In addition to the approximate P(adaptation),
  can derive the approximate proportion of
  emergence events due to within-host vs
  between-host adaptation

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Influence of covariation when overall R0 is fixed
I?
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Influence of covariation when overall R0 is fixed
I?

• Overall R0 3

1
0.9
0.8
0.7
0.6
Probability
0.5
0.4
0.3
0.2
0.1
0

-2
-1
0
1
2
10
10
10
10
10
Relative susceptibility of group 2
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Influence of covariation when overall R0 is fixed
S?

• Overall R0 3

0.8
0.7
0.6
0.5
Probability
0.4
0.3
0.2
0.1
0

-2
-1
0
1
2
10
10
10
10
10
Relative infectiousness of group 2
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Previous work on modelling emergence

• Antia et al, 2003 (between-host evolution,
  homogeneous population)
• If introduced strain has R0 lt 1, ultimate
  emergence is more likely as R0 approaches 1.
• Andre Day, 2005 (within- and between-host,
  homogeneous pop.)
• Duration of infection can be as important as R0.
• Yates et al, 2006 (between-host only,
  heterogeneous population without covariation
  between parameters)
• Host heterogeneity in susceptibility or
  infectiousness alone has little effect on
  emergence.

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• But CD4 count isnt the whole story HIVs impact
  on invasive bacterial infection is thought to be
  mediated by mononuclear innate immune cells
  (macrophages, dendritic cells, etc)
• Results are indicating that HAART (and resulting
  elevated CD4 counts) do not reduce risk of
  bacterial infections. (Noursadeghi et al, Lancet
  Inf Dis 2006)