modelling the role of household versus community transmission of tb in zimbabwe georgie hughes...
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Modelling the role of household versus community transmission of TB in
Zimbabwe Georgie Hughes
Supervisor: Dr Christine Currie
(University of Southampton)
In collaboration with: Dr Elizabeth Corbett
(London School of Hygiene and Tropical Medicine & Biomedical Research and Training Institute, Zimbabwe)
Overview of Presentation
Background- TB and HIV epidemiology
Previous TB Modelling - Deterministic Compartmental Models - Why more modelling is needed
The Harare Data
The Research- What am I doing? Why? How?
Validation and Sensitivity Analysis
Future Work
Tuberculosis
What is Tuberculosis?
• Tuberculosis is the most common major infectious disease today
• A person with Tuberculosis can either have an infection or Tuberculosis disease
• Symptoms include coughing, chest pain, fever, chills, weight loss and fatigue
• Tuberculosis is caught in a similar way to a cold
Tuberculosis (TB)
Facts:
TB infects one third of the world’s population
TB results in 2 million deaths annually, mostly in developing countries
The highest number of estimated deaths is in the South-East Asia Region (35%), but the highest mortality per capita is in the Africa Region
Human Immunodeficiency Virus (HIV)
What is HIV?
HIV is the virus that leads to AIDS (Acquired Immune Deficiency Syndrome)
The HIV virus weakens the body’s ability to fight infections
When the immune system is significantly weakened sufferers will get “opportunistic” infections which are life threatening
HIV and TB: A Dual Epidemic
TB is one of the leading causes of illness and death among AIDS sufferers in developing countries.
The two diseases fuel each other:
A person infected with TB has a risk of progression to “active” TB of only 10% over their lifetime
A person infected with TB and HIV has a risk of progression to “active” TB which increases to 10% each year
“We cannot win the battle against AIDS if we do not also fight TB. TB is too often a death sentence for people with AIDS. It does not have to be this way. We have known how to cure TB for more than 50 years.”
Nelson Mandela, July 2004
TB Incidence per 100,000 Worldwide
2005
WHO
<10
10<50
50<100
100<300
>=300
TB Incidence per 100,000 Worldwide
2005
WHO<10
10<50
50<100
100<300
>=300
2005
Estimated HIV Prevalence in TB Cases
0 - 4
5 - 19
20 - 49
50 or more
HIV prevalence in TB cases, 15-49 years (%)
No estimate 2003
WHO
0
100
200
300
400
500
600
700
800
0 5 10 15 20 25 30 35 40 45
HIV Prevalence (%)
TB
Inci
den
ce p
er 1
00,0
00Relationship Between TB and HIV
Countries in Sub-Saharan Africa
Botswana
Swaziland
Zimbabwe
Progress Report
Background
Previous TB Modelling
The Harare Data
The Research
Validation and Sensitivity Analysis
Future Work
Modelling TB Control Strategies
• Previous models have used assumptions about efficacy that cannot be validated due to a lack of data
• An iterative approach using modelling of both the theoretical intervention and actual trial data needed
There is still a need to identify TB control strategies that are effective in high
HIV prevalence settings
Previous Models
The majority of models have been
Deterministic Compartmental Models
The population is divided into epidemiological classes, for example:
Susceptibles (S)
Exposed/Latent (E)
Infectious (I)
Treated (T)
DCM Models An Example:
Differential Equations are used
to move proportions of the
population through the stages
Why is More Modelling Needed?
There is still a need to identify TB control strategies that are effective in high HIV prevalent settings
The current policy was developed in an era of low HIV prevalence
The impact of the HIV epidemic on the relative importance of household versus community transmission has not been fully assessed
DCMs are an unsuitable method for investigating interventions at the household level
Why is More Modelling Needed?
There is still a need to identify TB control strategies that are effective in high HIV prevalent settings
The current policy was developed in an era of low HIV prevalence
The impact of the HIV epidemic on the relative importance of household versus community transmission has not been fully assessed
DCMs are an unsuitable method for investigating interventions at the household level
Why is More Modelling Needed?
There is still a need to identify TB control strategies that are effective in high HIV prevalent settings
The current policy was developed in an era of low HIV prevalence
The impact of the HIV epidemic on the relative importance of household versus community transmission has not been fully assessed
DCMs are an unsuitable method for investigating interventions at the household level
Why is More Modelling Needed?
There is still a need to identify TB control strategies that are effective in high HIV prevalent settings
The current policy was developed in an era of low HIV prevalence
The impact of the HIV epidemic on the relative importance of household versus community transmission has not been fully assessed
DCMs are an unsuitable method for investigating interventions at the household level
Why are DCMs inadequate?
DCMs don’t allow the mechanics of transmission to be explored
Due to the complexity of the epidemiology a model is needed which allows for the various complexities to be incorporated
A Discrete Event Simulation (DES) model would allow for the
more intricate details of transmission to be understood
Progress Report
Background
Previous TB Modelling
The Harare Data
The Research
Validation and Sensitivity Analysis
Future Work
The Harare Data
Periodic intervention
to 42 neighbourhoods Door-to-door
enquiry or a mobile TB clinic
Diagnosis based on sputum
microscopy Interview householdhead to identify
previous TB diseaseevents
The Harare Data
The Harare data will provide cross sectional data on:
• The size and location of every household• The number of inhabitants• Their ages• Their poverty indicator• TB Status• HIV Status• Short term trends in TB Incidence following interventions
The Baseline Data
The baseline data was received in Access
Enabled us to look at the household distribution
Data had some surprises!
Being able to communicate with DETECTB was extremely helpful
A Data Driven Model
Observed Data
TB & HIV Modelling Literature
Expert Opinion
Health Literature
Run Model
Set Parameters
Model Output & Sensitivity Analysis
Progress Report
Background
Previous TB Modelling
The Harare Data
The Research
Validation and Sensitivity Analysis
Future Work
Epidemiological Issues to be addressed
Heterogeneity• Age Dependency• Gender• Non Homogeneous Mixing
Endogenous Reinfection Variable lengths of latency and infectiousness Immigration Poverty HIV
The Research
What am I doing?
What’s that?
Involves moving individuals through the model who each have their own attributes, disease characteristics and contact network
Developing a DES Household
Transmission Model
The Research
Why?
To understand:
• The role of household versus community transmission of both TB and HIV
The model will show the limits and potential impact of increasing
case-finding on TB in high HIV prevalent populations
The DES Model
How?
• Built an individual-based discrete event simulation model in C++
• Distributions are used to describe the progression of an individual through the model
• A static household structure
• Assume increased contact within households
• HIV is not modelled explicitly
• Children are represented in the model
Epidemiological Issues Addressed So Far
Homogeneity Age Dependency• Gender Non Homogeneous Mixing
Endogenous Reinfection Variable lengths of latency and infectiousness Immigration Poverty HIV
Progress Report
Background
Previous TB Modelling
The Harare Data
The Research
Validation and Sensitivity Analysis
Future Work
Validation
Validation
0
200
400
600
800
1000
1200
1650 1700 1750 1800 1850 1900 1950 2000 2050
Year
TB
Inci
den
ce p
er 1
00,0
00
Validation
0
100
200
300
400
500
600
700
800
900
1000
1950 1960 1970 1980 1990 2000 2010 2020
Year
TB
Inci
den
ce p
er 1
00,0
00
TB Incidence Data Average TB Incidence Model Output
Validation
0%
5%
10%
15%
20%
25%
30%
1980 1985 1990 1995 2000 2005 2010 2015 2020
Year
HIV
Pre
vale
nce
(%
)
HIV Prevalence Data
Average HIV Prevalence Model Output
Sensitivity Analysis
Observed Data
TB & HIV Modelling Literature
Expert Opinion
Health Literature
Run Model
Set Parameters
Model Output & Sensitivity Analysis
Experimental Design
Factors
Time of Late Stage HIV Size of Household HIV reactivation rate HIV Survival Distribution
= 1.6, = 1.6, = 1.6, = 1.6,
Factor Number Factor Description - +
1 Time of Late Stage HIV 4 yrs 6 yrs
2 Size of Household 3.99 5.5
3 HIV Reactivation Rate 0.1 0.33
4 HIV Survival Distribution (Weibull)
= 1.6, =11.18mean = 10.07 yrs
= 1.6, =13.38mean = 12 yrs
Response
Model Fit Pre-HIV TB Incidence
Level Peak value of TB
Incidence curve Timing of TB epidemic Gradient of the TB
Incidence increase
+++16
++-15
+-+14
+--13
+++12
++-11
+-+10
+--9
-++8
-+-7
--+6
---5
-++4
--+-3
---+2
----1
4321Design
+++16
++-15
+-+14
+--13
+++12
++-11
+-+10
+--9
-++8
-+-7
--+6
---5
-++4
--+-3
---+2
----1
4321Design
Progress Report
Background
Previous TB Modelling
The Harare Data
The Research
Validation and Sensitivity Analysis
Future Work
We have described a model of TB and HIV that will be used to assess the effectiveness of different case detection strategies for TB
Future Work:
Incorporate the various epidemiological issues
Use Harare Data to inform model parameters
Experimentation and Scenario Analysis
Screen Shot
Heterogeneity• Age Dependency• Gender• Non Homogeneous Mixing
Model Schematic
Susceptibles
LatentFast Latent
Active Infectious DiseaseTreatment
Recovered
Fast Latent
Treatment Active Infectious Disease
Self CureSelf Cure
Model Schematic
Fast Latent
0.2
0.6 1
1.4
1.8
2.2
2.6 3
3.4
3.8
4.2
4.6 5
Years until Active Disease will Develop
xexf )(
The Exponential Distribution
iii tPP )(
The observed fast latent distribution can be described by the equation:
ni ,...,1
Maximum Likelihood Distribution
iii tPP )(Therefore..
where
and
),0(~ 2 Ni
)( iii tPP
n
i
n
iiii tPPL
1 1
2
22)(
2
1exp
2
1)()(
The Likelihood function:
n
iii tPPn
nLOGLIK
1
2
2)(
2
1log)2log(
2)(
The Log Likelihood function:
Fast Latent
0.2
0.6 1
1.4
1.8
2.2
2.6 3
3.4
3.8
4.2
4.6 5
Years until Active Disease will Develop
Fast Latent
Years until Active Disease will Develop
HIV Survival
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52
Survival Time in Years
Distribution of Household Size
1 3 5 7 9 11 13 15 17 19 21 23 25
Number in Household
Distribution of Household Size
1 3 5 7 9 11 13 15 17 19 21 23 25
Number in Household