1. 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)

2. 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

3. 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

4. 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

5. 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

6. 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

7. “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

8. TB Incidence per 100,000 Worldwide 2005 WHO <10 10<50 50<100 100<300 >=300

9. TB Incidence per 100,000 Worldwide 2005 WHO <10 10<50 50<100 100<300 >=300 2005

10. 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

11. Relationship Between TB and HIV Countries in Sub-Saharan Africa Botswana Swaziland Zimbabwe

12. Progress Report  Background  Previous TB Modelling  The Harare Data  The Research  Validation and Sensitivity Analysis  Future Work

13. 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

14. 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)

15. DCM Models An Example: Differential Equations are used to move proportions of the population through the stages

16. 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

17. 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

18. 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

19. 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

20. 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

21. Progress Report  Background  Previous TB Modelling  The Harare Data  The Research  Validation and Sensitivity Analysis  Future Work

22. The Harare Data Periodic intervention to 42 neighbourhoods Door-to-door enquiry or a mobile TB clinic Diagnosis based on sputum microscopy Interview household head to identify previous TB disease events

23. 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

24. 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

25. A Data Driven Model Observed Data TB & HIV Modelling Literature Expert Opinion Health Literature Run Model Set Parameters Model Output & Sensitivity Analysis

26. Progress Report  Background  Previous TB Modelling  The Harare Data  The Research  Validation and Sensitivity Analysis  Future Work

27. Epidemiological Issues to be addressed  Heterogeneity Age Dependency Gender Non Homogeneous Mixing  Endogenous Reinfection  Variable lengths of latency and infectiousness  Immigration  Poverty  HIV

28. 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

29. 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

30. 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

31. Epidemiological Issues Addressed So Far  Homogeneity Age Dependency Gender Non Homogeneous Mixing Endogenous Reinfection Variable lengths of latency and infectiousness  Immigration  Poverty HIV

32. Progress Report  Background  Previous TB Modelling  The Harare Data  The Research  Validation and Sensitivity Analysis  Future Work

33. Validation

37. Sensitivity Analysis Observed Data TB & HIV Modelling Literature Expert Opinion Health Literature Run Model Set Parameters Model Output & Sensitivity Analysis

38. Experimental Design Factors  Time of Late Stage HIV  Size of Household  HIV reactivation rate  HIV Survival Distribution = 1.6, Factor NumberFactor Description-+ 1Time of Late Stage HIV4 yrs6 yrs 2Size of Household3.995.5 3HIV Reactivation Rate0.10.33 4HIV Survival Distribution (Weibull) = 1.6, =11.18 mean = 10.07 yrs = 1.6, =13.38 mean = 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

39. Progress Report  Background  Previous TB Modelling  The Harare Data  The Research  Validation and Sensitivity Analysis  Future Work

40. 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

41. The End! G.R.Hughes@soton.ac.uk http://www.maths.soton.ac.uk/postgraduates/Hughes

42. Screen Shot  Heterogeneity Age Dependency Gender Non Homogeneous Mixing

43. Model Schematic Susceptibles Latent Fast Latent Active Infectious Disease Treatment Recovered Fast Latent Treatment Active Infectious Disease Self Cure

44. Model Schematic

45. Fast Latent

46. The Exponential DistributionThe observed fast latent distribution can be described by the equation: Maximum Likelihood Distribution Therefore.. where and The Likelihood function:The Log Likelihood function:

47. Fast Latent

49. HIV Survival

50. Distribution of Household Size