HIV transmission dynamics Nargesalsadat Dorratoltaj MSc MPH Department of Population Health Sciences Virginia Tech Blacksburg VA Stephen Eubank PhD Virginia Bioinformatics Institute Virginia Tech Blacksburg ID: 793900
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Slide1
Multi-scale modeling of HIV transmission dynamics
Nargesalsadat
Dorratoltaj
, M.Sc., MPH
Department of Population Health Sciences, Virginia Tech, Blacksburg,
VA
Stephen Eubank, PhD
Virginia Bioinformatics Institute, Virginia Tech, Blacksburg,
VA
Josep
Bassaganya-Riera
, PhD
Virginia Bioinformatics Institute, Virginia Tech, Blacksburg,
VA
Hazhir
Rahmandad
, PhD
Department of Industrial and Systems Engineering, Virginia
Tech, Arlington VA
Margaret
O'Dell,
MD
New River Health District, Virginia Department of Health, Christiansburg,
VA
Kaja Abbas,
PhD
Department of Population Health Sciences, Virginia Tech, Blacksburg, VA
Slide2Conflict of Interest: NoneWe declare that we have no conflict of interest, and we comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines.
Slide3Learning objectivesFormulate and analyze the within host transmission dynamics of HIV using differential equations.
Design and analyze the between host transmission dynamics of HIV using a network model
.
Design a
multiscale
model connecting the within host transmission dynamics and between host transmission dynamics of HIV.
Slide4Study Objective
Slide5Study ObjectiveDevelop a multi-scale immunoepidemiological model of
HIV with focus on the impact of antiretroviral treatment
interruptions:
Understand the within host HIV viral and immune dynamics at the individual
level
Understand the epidemiological dynamics of HIV transmission in the population
Connect the
individual
level model
to
the epidemiological level and build a multi-scale model of HIV transmission and dynamics.
Slide6Background
Slide7Human Immunodeficiency Virus (HIV)HIV is a lentivirus
(a
subgroup of retrovirus
).
The
primary target of HIV are helper T cells, Macrophages, and dendritic cells
.
It causes acquired immunodeficiency syndrome (AIDS), which tends to progressive failure of the immune system.
AIDS allows opportunistic infections and cancers to thrive.
Transmission happens through blood, semen, vaginal fluid, or breast milk
.
Without treatment, the average survival time is 9 to 11 years after infection.
Slide8Global epidemiology of HIV
Slide9HIV/AIDS treatment:Highly Active Antiretroviral Therapy (HAART)
First introduced in 1996 with focus on the effect of combinational therapy for HIV treatment
Kumar and
Herbein
, 2014,
Molecular and Cellular Therapies
Slide10Highly Active Antiretroviral Therapy (HAART) coverage
Slide11Highly Active Antiretroviral Therapy (HAART)
Benefits:
A milestone in HIV treatment,
Causes significant decline in mortality among HIV+ patients
.
Risks and problems:
Drug resistance
Treatment fatigue
Drug toxicity
Adherence issues
High cost
Life style issues
Different studies show that 6 to 51% of HIV-positive patients interrupt their treatment because of the following reasons:
Forgetting
Traveling
Nausea and vomiting
Running out of pills
Losing or misplacing pills
No confidence in effectiveness of pills
Or simply because:
They feel well
Slide12Periodic treatment interruptions: A possible solution for HAART adverse effects
Benefits
To control HAART adverse effects
To let the HIV wild type emerge
To improve drug adherence
S
ince 2003, different studies reported conflicting results:
Staccato
2003
SMART 2006
LOTTI 2009
Other smaller studies…
Risks
Viral rebounds
Increased person to person transmission
Increased risk of opportunistic co-infection
Slide13Currently, there are no good explanations for the clinical differences of previous studies.
We suggest mathematical models as a less expensive method to predict HIV treatment interruption impacts within host
.
Mathematical
models can prevent harmful clinical trails. They can
help experimentalists
in optimizing criteria selection
.
Choosing the
correct threshold
can be crucial to prevent adverse effects
of long term treatment and its interruption.
Public Health Significance
Slide14Immunoepidemiological modelImmunoepidemiology: Immunoepidemiology
studies the combined effect of the immunological dynamics at the individual levels and the epidemiological dynamics at the population level
.
Immunology:
Immunological
models analyze the within host dynamics between HIV virus, uninfected CD4+ T Cells, and Infected CD4+ T Cells.
Epidemiology:
Epidemiological
models, analyze the transmission dynamics between Susceptible, and Infected individuals during the acute, latent, and late stages of HIV.
What have been done so
far?
Within
and among host scales have been studied
separately.
Where is the knowledge
gap?
Understanding
HIV dynamics occurring across scales by developing
multi-scale
modeling is significant and novel.
/11
14
Slide15MethodsWithin host model of HIV immune-viral dynamicsBetween host model of HIV transmission in the population
Slide16MethodsWithin host model of HIV immune-viral dynamicsBetween host model of HIV transmission in the population
Slide17Conceptual framework of HIV dynamics within host
Slide18HIV dynamics within host: Mathematical model
# Uninfected T
cells
# Infected T cells
Parameter symbol
Description
Value
Range
Unit
Reference
Uninfected T cell birth rate
10
(5,36)
mm
-3
day
-1
Krischner and Perelson (1995)
HIV
transmission
rate
4.57x10
-5
(10
-8
,10
-2
)
mm
3
day
-1
Hadjiandrea
et al (2007)
Uninfected T cell death rate
0.01
(0.01,0.02)
day
-1
Hadjiandrea
et al (2007)
Infected cell death rate
0.4
(0.24,0.7)
day
-1Krischner and Perelson (1995)Virus production rate45(37,500)day-1Hadjiandrea et al (2007)Virus clearance rate2.4(2.4,50)day-1Hadjiandrea et al (2007)
Parameter symbol
Description
Value
Range
Unit
Reference
Uninfected T cell birth rate
10
(5,36)
mm
-3
day
-1
Krischner and Perelson (1995)
HIV
transmission
rate
4.57x10
-5
(10
-8
,10
-2
)
mm
3
day
-1
Hadjiandrea
et al (2007)
Uninfected T cell death rate
0.01
(0.01,0.02)
day
-1
Hadjiandrea
et al (2007)
Infected cell death rate
0.4
(0.24,0.7)
day
-1
Krischner and Perelson (1995)
Virus production rate
45
(37,500)
day
-1
Hadjiandrea
et al (2007)
Virus clearance rate
2.4
(2.4,50)
day
-1
Hadjiandrea
et al (2007)
Slide19Adding treatment to the model
Uninfected CD4
cells:
Infected
CD4
cells:
Infectious
viruses:
Reverse Transcriptase Inhibitors efficacy
: Protease Inhibitors efficacy
Kumar and
Herbein
, 2014,
Molecular and Cellular Therapies
Slide20MethodsWithin host model of HIV immune-viral dynamicsBetween host model of HIV transmission in the population
Slide21A network model to simulate the sexual partnership Network Characteristics:People(Nodes) characteristics:
Sex
Age
# of partnerships (edges)
Time to HAART initiation after HIV transmission
Co-infection?
Partnership (Edges) characteristics:
Type( Spousal or Non-spousal)
Heterosexual partnership (Bipartite)
Chance of HIV transmission from male to female equals the chance of transmission from female to male ( Not-directed)
Slide22Exponential family random graph model (ERGM) to predict the probability of a partnershipWhy?
Social networks are more clustered than random networks
Homophily
(People choose partners who are like them)
Transitivity (Friend of a friend)
ERGM is general and flexible
How?
Predict the probability of a partnership(edge) based on network statistics:
Total number of edges
Number of males and females in a monogamy or concurrent partnership (2 partnership)
Slide23ResultsWithin host model of HIV immune-viral dynamicsBetween host model of HIV transmission in the population
Slide24ResultsWithin host model of HIV immune-viral dynamicsBetween host model of HIV transmission in the population
Slide25HIV dynamics with no treatment
CD4 cell steady state :
455 cells per microliter
Viral load steady state:
261,950 copies per ml
Slide26HIV dynamics with combination treatment
Antiretroviral therapy is initiated at day=700 after HIV transmission.
Slide27HIV dynamics duringtime-based treatment
interruptions
Treatment interruption
periods: 200
days
Slide28HIV dynamics during time-based treatment interruptions
100 days interruption periods
30 days interruption periods
Slide29HIV dynamics during adaptive periodic treatment interruptions
Slide30HIV dynamics duringadaptive periodic treatment interruptions
Slide31ResultsWithin host model of HIV immune-viral dynamicsBetween host model of HIV transmission in the population
Slide32Fitted network model
Slide33Discussion
Slide34SummaryHIV-positive patients may interrupt their treatment because of treatment fatigue or adverse effects.
Currently,
there is no good explanation on how patients can go on treatment interruption without experiencing any harm.
Periodic treatment interruption can be
an option to
assist patients.
Mathematical models can help us recommend feasible strategies for treatment interruption.
Slide35Public Health Implications:More effective interruption threshold for future clinical trials.Less/non-harmful HAART interruption periods.
Slide36Limitations“All models are wrong, but some are useful.“George E. P.
Box
Simplified immune system
(No
CTL, Macrophage, Dendritic cells
).
No suggestion for the
acute
or AIDS
stages.
Treatment causes full CD4 recovery and virus elimination, without considering latent infection.
…
Slide37Future workAdd effects of other immune cells to the model.
Validate within host model with real world
data.
Simulate the impact of treatment interruption on HIV transmission in the population.
Apply the results
of within host and between host models to build an immunoepidemiological model of HIV treatment interruption.
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Slide39Acknowledgements
Public Health Program
Stanca
Ciupe
, PhD
Mathematics Department
Virginia Tech
Jessica M.
Conway, PhD
Department of Mathematics
The Pennsylvania State University
Slide40THANK YOUContact: Narges Dorratoltajnargesd@vt.edu