Stochastic Models of Early HIV Infection

Bernhard Konrad

 

Institute of Applied Mathematics

The University of British Columbia

 

March 30th, 2015

HIV/AIDS - a deadly pandemic

  • 35M individuals living with HIV today
  • 36M AIDS-caused deaths total (1.6M in 2012)
  • 2.3M new HIV infections in 2012

HIV/AIDS close to home

Canada

  • 70,000 HIV+ (2009)
  • 2,500 new diagnosis every year
  • one new infection every 3h

BC

  • 400 new diagnosis every year
  • 3,500 unaware of infection (25%)

Epidemiology with BCCDC

Survey Data from epidemiologists at the BCCDC in Vancouver:

  • ManCount
  • SexNow
  • GetCheckedOnline

Data mining, visualization, and modelling to gain actionable insight and predict the outcome of health interventions in the Vancouver HIV MSM epidemic.

Immunology of HIV infection

  • several weeks for potent antibody response to mount
  • symptoms at infection very mild or completely absent

Importance of Early Infection

  • Extremely high viral load
  • Sexually active
  • Unaware of infection

Importance of Early Infection

  • Different HIV tests look for different signals...
  • ...and become positive at different times

Importance of Early Infection

  • Work so far has focused on calculating relative time delay between Fiebig Stages

Research Question

  1. How long is the window period?
  2. Given a negative HIV test t days after possible exposure, how likely is an infection?
  3. When to schedule second, confirmatory test?

Mathematical formulation

  1. Distribution of the length of the window period?
  2. What is the probability that the number of virions is large enough to be detectable by a HIV test at time t?
  3. What is the probability that a patient is infected, given a negative test result at time t?

Data

  • Plasma samples from regular donors
  • Stored before putting put in use
  • Removed if any infection detected in a later sample
  • Very valuable in research for benchmarking new tests

Data

Data

  • Data collected (bought by BCCDC) from several sources
  • Stored in several csv files
  • Usually several tests for the same patient sample
    • duplicates (easy)
    • lower and upper detection limits
    • different HIV tests (usually trust "better" test)
    • correctly average if tests disagree
  • Importance of visualization

Mathematical Model

  • Detection limit 50 copies/ml
  • Inoculum size assumed to be small for most types of exposure (eg. 100 virions)
  • Increase of viral load roughly exponential

Simplest model

  • Linear regression
  • Extrapolate to the left until y=inoculum size
  • Read off time of exposure

Mathematical Model

Simplest version: Fit linear regression, extrapolate

Mathematical Model

Simplest version:

Fit linear regression, extrapolate

Major shortcomings of linear regression:

  • HIV infection is a stochastic event!
  • Viral growth stochastic
  • Most exposures do not lead to infection

 

Only 1/100-1/1000 (depending on exposure)

Stochastic model

Simplest stochastic analogue: Birth-death process

V \rightarrow V+1
V→V+1V \rightarrow V+1

Birth rate:

V \rightarrow V-1
V→V−1V \rightarrow V-1

Clearance rate:

Leads to exponential growth with rate b-c

Birth-death process

  • Average growth = [birth] - [clearance]
  • [clearance] = literature value (known)
  • [birth] = [clearance] + [fitted average growth]

Gillespie Algorithm

t = 0
N = INOCULUM_SIZE

while N < DETECTION_LIMIT:
    # Time to next event is exponentially distributed
    t += EXP(bN+cN)

    # Likelihood of event is proportional to reaction rates
    if RAND(0,1) < (b/b+c):
        # birth event
        N = N+1
    else:
        # clearance event
        N = N-1

return t

Birth-death process

  • Run simulations many times, for each growth rate
  • Obtain distribution of length of window period

Technical: Avoid simulations

Instead of millions of Gillespie simulations, my research group found a way to calculate pdfs semi-analytically

  • Much faster to compute
  • Allows valuable insight
  • Beautiful application of complex analysis :)

Technical: Avoid simulations

P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)
P(v,v0,t)=Prob(V(t)=v∣V(0)=v0)P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)

Set

\frac{d}{dt}P(v, v_0, t) = b(v-1)P(v-1, v_0, t) + c(v+1)P(v+1, v_0, t) - (b+c)vP(v, v_0, t)
ddtP(v,v0,t)=b(v−1)P(v−1,v0,t)+c(v+1)P(v+1,v0,t)−(b+c)vP(v,v0,t)\frac{d}{dt}P(v, v_0, t) = b(v-1)P(v-1, v_0, t) + c(v+1)P(v+1, v_0, t) - (b+c)vP(v, v_0, t)

Then we can easily derive the Master equation (balance equation)

v-1 and single birth

v+1 and single clearance

Leave state v by birth or clearance event

Knowing P means understanding everything!

Technical: Avoid simulations

\frac{d}{dt}P(v, v_0, t) = b(v-1)P(v-1, v_0, t) + c(v+1)P(v+1, v_0, t) - (b+c)vP(v, v_0, t)
ddtP(v,v0,t)=b(v−1)P(v−1,v0,t)+c(v+1)P(v+1,v0,t)−(b+c)vP(v,v0,t)\frac{d}{dt}P(v, v_0, t) = b(v-1)P(v-1, v_0, t) + c(v+1)P(v+1, v_0, t) - (b+c)vP(v, v_0, t)
P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)
P(v,v0,t)=Prob(V(t)=v∣V(0)=v0)P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)

Set

A little helper: The probability generating function

G(v_0, t, z) = \sum_v P(v, v_0, t)z^v
G(v0,t,z)=∑vP(v,v0,t)zvG(v_0, t, z) = \sum_v P(v, v_0, t)z^v
\frac{\partial}{\partial t}G(1, t, z) = b(G(1, t, z))^2 + c - (b+c)G(1, t, z)
∂∂tG(1,t,z)=b(G(1,t,z))2+c−(b+c)G(1,t,z)\frac{\partial}{\partial t}G(1, t, z) = b(G(1, t, z))^2 + c - (b+c)G(1, t, z)
G(1, 0, z) = z
G(1,0,z)=zG(1, 0, z) = z

Master equation: infinitely many ODEs!

Use branching property to derive single ODE

Technical: Avoid simulations

P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)
P(v,v0,t)=Prob(V(t)=v∣V(0)=v0)P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)

Set

Key observations:

P(v, v_0, t) = \frac1{v!}\left.\frac{\partial^v G(v_0, t, z)}{\partial z^v}\right|_{z = 0}
P(v,v0,t)=1v!∂vG(v0,t,z)∂zv∣z=0P(v, v_0, t) = \frac1{v!}\left.\frac{\partial^v G(v_0, t, z)}{\partial z^v}\right|_{z = 0}
\frac{\partial}{\partial t}G(1, t, z) = b(G(1, t, z))^2 + c - (b+c)G(1, t, z)
∂∂tG(1,t,z)=b(G(1,t,z))2+c−(b+c)G(1,t,z)\frac{\partial}{\partial t}G(1, t, z) = b(G(1, t, z))^2 + c - (b+c)G(1, t, z)
G(v_0, t, z) = \sum_v P(v, v_0, t)z^v
G(v0,t,z)=∑vP(v,v0,t)zvG(v_0, t, z) = \sum_v P(v, v_0, t)z^v
G(v_0, t, z) = (G(1, t, z))^{v_0}
G(v0,t,z)=(G(1,t,z))v0G(v_0, t, z) = (G(1, t, z))^{v_0}

(branching property)

Technical: Avoid simulations

P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)
P(v,v0,t)=Prob(V(t)=v∣V(0)=v0)P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)

Set

P(v, v_0, t) = \frac1{v!}\left.\frac{\partial^v G(v_0, t, z)}{\partial z^v}\right|_{z = 0}
P(v,v0,t)=1v!∂vG(v0,t,z)∂zv∣z=0P(v, v_0, t) = \frac1{v!}\left.\frac{\partial^v G(v_0, t, z)}{\partial z^v}\right|_{z = 0}
G(v_0, t, z) = \sum_v P(v, v_0, t)z^v
G(v0,t,z)=∑vP(v,v0,t)zvG(v_0, t, z) = \sum_v P(v, v_0, t)z^v

Instead of taking millions of derivatives (numerically unstable)...

...use Cauchy's integral formula!

f^{(n)}(0) = \frac{n!}{2\pi i} \oint_\gamma \frac{f(z)}{z^{n+1}}dz
f(n)(0)=n!2πi∮γf(z)zn+1dzf^{(n)}(0) = \frac{n!}{2\pi i} \oint_\gamma \frac{f(z)}{z^{n+1}}dz

Technical: Avoid simulations

P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)
P(v,v0,t)=Prob(V(t)=v∣V(0)=v0)P(v, v_0, t) = Prob(V(t)=v\,|\,V(0)=v_0)

Set

G(v_0, t, z) = \sum_v P(v, v_0, t)z^v
G(v0,t,z)=∑vP(v,v0,t)zvG(v_0, t, z) = \sum_v P(v, v_0, t)z^v
f^{(n)}(0) = \frac{n!}{2\pi i} \oint_\gamma \frac{f(z)}{z^{n+1}}dz
f(n)(0)=n!2πi∮γf(z)zn+1dzf^{(n)}(0) = \frac{n!}{2\pi i} \oint_\gamma \frac{f(z)}{z^{n+1}}dz
P(v, v_0, t) = \frac1{2\pi}\int_0^{2\pi} G(v_0, t, e^{i\varphi})\,e^{-iv\varphi}\,d\varphi
P(v,v0,t)=12π∫02πG(v0,t,eiφ)e−ivφdφP(v, v_0, t) = \frac1{2\pi}\int_0^{2\pi} G(v_0, t, e^{i\varphi})\,e^{-iv\varphi}\,d\varphi
  • Integration is numerically stable
  • Resulting pdf for P is exact (Gillespie algorithm only gives approximation)

The hook is the hook

  1. How long is the window period?
  2. Given a negative HIV test t days after possible exposure, how likely is an infection?
  3. When to schedule second, confirmatory test?

Regardless of the method for the computation (Gillespie or semi-analytic), one major flaw remains:

 

HIV infection is very unlikely (0.1%-1%), but all observed patients became infected!

 

Extremely biased training set!

Maybe the virus was initially just "lucky" to grow faster than average!?

The hook is the hook

Coin-flip intuition

Heads: +1.1 points

   Tails:  -1 point

A) Start with 20 points

B) Start with 1 point

 

Lose when <= 0 points

The hook is the hook

P(V(t) = v\,|\,V(0)=v_0, \color{red}{V(\infty) \neq 0})
P(V(t)=v∣V(0)=v0,V(∞)≠0)P(V(t) = v\,|\,V(0)=v_0, \color{red}{V(\infty) \neq 0})

Fit conditioned process  to match the bias in the data

Virus not spontaneously cleared

Using Bayes rule and Markov property

P(V(t)=v\,|\,V(0)=v_0, V(\infty)\neq 0) = P(v, v_0, t)\frac{P(V(\infty)\neq 0\,|\,V(0) = v)}{P(V(\infty)\neq 0\,|\,V(0) = v_0)}
P(V(t)=v∣V(0)=v0,V(∞)≠0)=P(v,v0,t)P(V(∞)≠0∣V(0)=v)P(V(∞)≠0∣V(0)=v0)P(V(t)=v\,|\,V(0)=v_0, V(\infty)\neq 0) = P(v, v_0, t)\frac{P(V(\infty)\neq 0\,|\,V(0) = v)}{P(V(\infty)\neq 0\,|\,V(0) = v_0)}
= P(v, v_0, t)\color{red}{\frac{1-q^v}{1-q^{v_0}}}
=P(v,v0,t)1−qv1−qv0= P(v, v_0, t)\color{red}{\frac{1-q^v}{1-q^{v_0}}}

where q is likelihood that exposure by single virion leads to infection

The hook is the hook

Results

For actual risk calculations, replace b with more detailed model of viral replication

Fit unconditioned and conditioned model to patient data, assuming different risk scenarios

Results

Distribution of the length of the window period

Due to hook: Shorter than previously expected!

Results

If infected: Averaged likelihood of detectable viral load at time t

Results

Likelihood of infection given a negative test at time t

days of neg test --- remaining risk  
       5.2 --- 0.475%
       5.9 --- 0.450%
       7.2 --- 0.375%
       9.2 --- 0.250%
     12.8 --- 0.125%
     18.8 --- 0.050%
     22.2 --- 0.025%

Summary

  • Extracted and combined early viral load growth rates from various datasets

 

  • Developed solid mathematical framework that is superior to previous methods

 

  • Showed that excluding stochastic effects may lead to overestimation of length of window period

 

  • Quantified uncertainty of early negative HIV test

 

  • Gave recommendations to clinicians that help to find and treat real infections sooner

Future Work

  • Help clinicians communicate stochastic results
  • Include uncertainty from different HIV tests
  • Specify model for different types of exposure
  • Incorporate PEP and PrEP
  • Confirm stochastic growth predictions with other data (eg. non-human primates, epidemics) and in experiments (eg. E. coli)
  • Include information about the number of founder strains

Thank you!

Other PhD work

  • Pre- and Post-Exposure Prophylaxis in early HIV infection
  • Engineering a new anti-Malaria agent
  • Math Education Resources

PrEP and PEP in early HIV

  • Mathematically similar tools
  • Trojan horse theory for dendritic cells
  • Reverse transcriptase inhibitors better than protease inhibitors
  • 2 weeks PrEP about as good as current recommendation of 4 weeks

Malaria

Built on Nature paper:

Engineering anti-malaria agent that stops onwards transmission in mosquitoes

Trade-off between virulence and environmental effect

Math Education Resources

Lead to of volunteers to build free webapp with 1500 solutions to previous UBC Math exam questions

  • One-vs-all SVC to predict topic of a newly added question (NLP)
  • Recommendation algorithm to suggest next question based on course, peers, and difficulty

Stochastic Models of Early HIV Infection

By Bernhard Konrad

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Stochastic Models of Early HIV Infection

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