Mathematical modelling and spatial data analysis to inform TB care and control strategies in high TB incidence settings

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9.0 million people developed tuberculosis (TB) in 2013, with 1.5 million dying. Detecting TB disease at an early stage minimises the harm it causes to a person's health, and reduces the number of other people whom they infect. In richer countries with little TB, anyone who has had extended contact with someone with TB is likely to be tested. Poorer countries with much higher rates of TB mostly rely on people going to health centres themselves when they have symptoms of TB. Screening the general population for TB results in cases being detected earlier, but is too expensive to be widely used in high-TB countries. Developing a better understanding of the spatial distribution of TB will enable screening to be targeted at areas where it will have the greatest effect.I will look at TB in four settings with moderate or high levels of TB: Blantyre city, Malawi; Karonga district, Malawi; Zambia; and Western Cape Province, South Africa. There are three main questions I will explore:1) I will identify areas within the four settings where rates of TB are highest, and determine what characteristics of areas are associated with high levels of TB. For instance, TB is often concentrated in the poorest, most crowded areas of cities, and/or in areas with high rates of HIV.2) Most TB screening programs use one of two approaches: offering quick and convenient screening at a set location (which may change on a regular basis), or screening people at their own homes (either because they live with someone who has been diagnosed with TB, or because screening is being offered to all households in an area). In the former case, uptake of screening is likely to be highest amongst people living close to a screening location, and drop off as the distance increases. Very little is known about how quickly screening rates drop off with distance however, or how quickly levels of undiagnosed TB increase again after a screening program has stopped. I will investigate these questions using data from a completed trial of two intervention strategies in Zambia and South Africa. 3) It is possible that data on the household locations of people diagnosed with TB can be used to develop more cost-effective interventions against TB. At the moment, the World Health Organization recommends testing all people who live in the same house as a TB patient. Testing people in their neighbourhood may also be beneficial however, for two reasons. Firstly, M. tb. (the bacteria that causes most TB disease) is infectious and the patient may therefore have transmitted M. tb. to or caught M. tb. from someone who lives nearby. Secondly, certain factors such as HIV or malnutrition increase the odds of someone having TB, and these factors are often locally clustered. I will use data from the settings listed above (which include the dates and household locations of all people diagnosed with TB) to estimate the number of TB cases that could have been detected earlier if neighbourhood screening had been carried out. I will see if cost-effectiveness can be improved by only screening the neighbourhoods of patients with certain characteristics, for instance HIV positive cases. Finally, I will use computer simulations to estimate the number of new TB cases that could be prevented by screening.The planned research will give us a much better understanding of how the distribution of TB varies within high-TB cities and districts. It also will improve our knowledge of how current interventions against TB work at a small scale, and will suggest new interventions that can be used to reduce TB in resource poor settings.