Whole genome sequence (WGS) data from tuberculosis patients have the potential to greatly improve our understanding of Mycobacterium tuberculosis (Mtb) transmission in high burden settings. It can be used to estimate who is transmitting to whom, and where transmission might be happening. It can also be used to improve our understanding of TB epidemiology. For instance, to what extent does ‘superspreading’ occur in TB? WGS datasets contain large amounts of missing data however, and data are likely to be missing not at random, with less infectiousness people more likely to be missed.

In this project, the student will develop a conceptual framework to map the limited molecular epidemiological dataset to the complete TB case population, and then develop a mathematical model of Mtb transmission and TB disease development, and the processes through which people do or do not enter WGS databases. The model will be fitted to sequencing and relevant metadata from Karonga, Malawi. Using the model, the student will explore the effects of missing data on model estimates, and obtain a better understanding of Mtb transmission patterns in high burden, sub-Saharan African settings.

The student should have a strong quantitative background, ideally with some experience of mathematical modelling of infectious diseases and/or molecular epidemiology.

This project would be supervised by Dr Nicky McCreesh (https://www.lshtm.ac.uk/aboutus/people/mccreesh.nicky), Dr Rein Houben (https://www.lshtm.ac.uk/aboutus/people/houben.rein), and Prof Judith Glynn (https://www.lshtm.ac.uk/aboutus/people/glynn.judith). The student will need to apply to the MRC LID programme (http://mrc-lid.lshtm.ac.uk/) and then will be able to choose this project. Students are encouraged to contact Nicky before applying.