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Modelling the HIV-Associated TB Epidemic and the Impact of Interventions Aimed at Epidemic Control (lay summary)

This is a lay summary of the article published under the DOI: 10.1007/978-3-030-29108-2_3

Published onApr 30, 2023
Modelling the HIV-Associated TB Epidemic and the Impact of Interventions Aimed at Epidemic Control (lay summary)

Mathematical models help us understand HIV and TB epidemics even better

Mathematical models are a big help to health scientists and policy makers wanting to better understand the situation of people living with both HIV and tuberculosis, and which of their plans to fight it work best.

An increase in people being infected with the HIV virus in the 1990s coincided with many more cases of tuberculosis (TB). This was especially true for countries in sub-Saharan Africa. By the late 1990s, for example, over 70% of Zimbabwe’s TB cases were reported among people also living with HIV.

The number of co-occurring HIV and TB cases have fortunately dropped since around 2004, after countries such as South Africa and Botswana began providing antiretroviral therapy (ART) to HIV patients. 

However, researchers predict that despite the availability of ARTs, HIV will continue to influence the occurrence and spread of TB for decades to come.

Around 10% of TB cases worldwide are still being diagnosed among people who also live with HIV. Some 74% of these cases are people from sub-Saharan Africa. People who die from TB (the disease is curable thanks to treatment) often also had HIV.

TB must therefore be kept in mind whenever plans are made to control HIV in so-called high-burden countries that experience many cases. And the same goes for HIV whenever TB is being discussed. 

Mathematical models help researchers to see whether their treatment plans are working and will continue to work into the future.

In 2019, health scientists from the UK, USA and South Africa wrote a chapter together in a book called HIV and Tuberculosis. They looked at the many modelling studies that have been done since the early 1990s to understand the co-occurrence of HIV and TB.  

They especially focussed on so-called dynamic models. These use data that show how infections transmit (spread) within specific populations, and how the risk of getting a disease depends on how widespread infections already are. It also looks at how the number of cases change in response to plans being put into place.

The authors included models in their review that first showed how TB and HIV infections increased over time, and how the emergence of HIV caused TB cases to increase considerably in the 1990s. The researchers also reflected on models that showed how using ARTs and other control methods have helped to bring this number down in populations.

They also reviewed the different uses and achievements of TB and HIV modelling, and made suggestions on how it can be used in future.

The researchers believe that despite TB and HIV together afflicting up to 1 million people worldwide each year, it has not received enough attention from modellers working on the spread of diseases (which is known as epidemiology).

The authors acknowledged that it is genuinely difficult and complex to try and handle two conditions together that are, on their own, very complicated to understand, control and treat.

To develop adequate models is therefore not an easy task. The job requires data to work with, and the combined team effort of skilled experts. They must, for instance, know which type of model to use for specific purposes.

Nevertheless, such models can help authorities and funders write good policies and plans about where and how they should spend money to control and prevent TB and HIV.

They can, for instance, help to locate specific hot spots in a community that are often the scene of high numbers of infections, because of tight social contact and poor ventilation. Relatively easy plans can then be put into place to prevent further infections from happening.

Modelling can provide health scientists and officials with greater insights into how the diseases spread, what the long term impact of control efforts such as ARTs are, and how the situation might look in future. 

The authors said such modelling work must also include other societal issues, such as urbanisation, whether people have access to enough nutritious food, and the rise of diabetes.


In this chapter, we focus on mathematical models of tuberculosis epidemiology (TB) that include interactions with HIV and an explicit representation of transmission. We review the natural history of TB and illustrate how its features are simplified and incorporated in mathematical models. We then review the ways HIV influences the natural history of TB, the interventions that have been considered in models, and the way these individual-level effects are represented in models. We then go on to consider population-level effects, reviewing the TB/HIV modelling literature. We first review studies whose focus was on purely epidemiological modelling, and then studies whose focus was on modelling the impact of interventions. We conclude with a summary of the uses and achievements of TB/HIV modelling and some suggested future directions.


This summary is a free resource intended to make African research and research that affects Africa, more accessible to non-expert global audiences. It was compiled by ScienceLink's team of professional African science communicators as part of the Masakhane MT: Decolonise Science project. ScienceLink has taken every precaution possible during the writing, editing, and fact-checking process to ensure that this summary is easy to read and understand, while accurately reporting on the facts presented in the original research paper. Note, however, that this summary has not been fact-checked or approved by the authors of the original research paper, so this summary should be used as a secondary resource. Therefore, before using, citing or republishing this summary, please verify the information presented with the original authors of the research paper, or email [email protected] for more information.

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