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

The End!

G.R.Hughes@soton.ac.uk

http://www.maths.soton.ac.uk/postgraduates/Hughes

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