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Disease emergence in immunocompromised populations

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Title: Disease emergence in immunocompromised populations


1
Disease emergence inimmunocompromised populations
  • Jamie Lloyd-Smith
  • Penn State University

2
Africa a changing immune landscape
HIV prevalence in adult populations
How might this influence disease emergence?
3
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?

4
(No Transcript)
5
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.
6
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.
7
Pathogen fitness landscapes
adaptation
One-step adaptation
R0 in healthy population
adaptation
Two-step adaptation
R0 in healthy population
8
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.

9
Model equations 1 group, 1 strain
where, because of the large-population assumption
10
Model equations 1 group, 2 strains
11
Model equations 2 groups, 2 strains
12
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.

13
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
14
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.

15
Pathogen invasion, without evolution
Heterogeneous infectiousness only
  • See Lloyd-Smith et al, 2005.

16
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

17
Pathogen invasion co-varying parameters
  • Dashed lines infectiousness varies in duration

18
Pathogen invasion co-varying parameters
Population with heterogeneous infectiousness, I?
Prob. of invasion
R0 when cov(inf, susc) 0
19
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
20
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
21
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
22
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
23
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
24
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
25
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
26
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
27
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
28
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.)
29
HIV and acute respiratory infections
Alagiriswami Cheeseman, 2001
Evans et al, 1995
30
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
31
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

32
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

33
Additional material
34
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

35
Influence of covariation when overall R0 is fixed
I?
36
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
37
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
38
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.

39
  • 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)
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