Development of a Bayesian network approach for analysing social science and biological data: the case of antibiotic resistance.

Big data' on health is a growing resource and complex interdisciplinary challenge. The availability of large heterogeneous datasets which link data across diverse domains - such as mental health indicators and microbiological genomics - has the potential to unravel complex interplay between myriad elements in the individual life course and society in general2,3. To maximise this potential, there is pressing need for advanced methods for analysing these complex data. However, the analysis of such complex linked data, presents challenges, and traditionally applied statistical analyses often struggle with the data itself. Data can be noisy, and the noise non-random. Questionnaires are often answered in a
systematically biased way, and self-reporting of certain aspects are subject to poor recall, desirability, bias or inaccurate self-estimation. Data also often comes in categorical, rather than numerical forms, which can be problematic for some forms of statistical modelling. Finally, missing data, in terms of missing item responses and longitudinal attrition is common. Thus, we need more flexible and advanced techniques to tackle these issues.
In this PhD and MRes we will develop an innovative interdisciplinary solution by applying a network-based approach, typically using in analysing biological systems, to analyse integrated social science and biomedical data.
Bayesian networks have potential to address many technical modelling issues that social science data faces but have yet to be widely applied in social sciences. This is because it is necessary to develop both practical methodology and specific theoretical advances before they are a truly usable approach. Here, we will develop these advances (further details below).
This project will apply BN approaches to a pressing and complex global health challenge: antibacterial resistance (ABR) in three countries in East Africa. The potential harm that increasing levels of ABR will have on human health is vast. One of the world regions most vulnerable to the increase in antibiotic resistance is Africa where, in comparison to other regions of the world, the burden of infectious diseases is highest. A 'one health', interdisciplinary approach to understanding the problem, which stresses the interconnectedness of social and biological domains and spans the human, animal and environmental spheres has been proposed.However such a complex system is difficult to analyse without advanced tools. There may be multiple drivers are varying scales: individual behavioural, household, community and healthcare system levels. The relative importance and inter-relationships between these drivers in predicting levels of AMR at individual and community level, however, not well understood, and moreover are likely to be highly context specific
Research Questions
The research questions for this PhD are twofold, methodological and substantive:
1. Methodological: To develop BN structure learning to enable integrating of interdisciplinary big data across social sciences and health, specifically addressing:
a. Missing data
b. Categorical variables
c. Biased noise
2. Substantive focussed on a case study of communicable disease: using data from HATUA, to investigate the drivers of antibacterial resistance in urinary tract infections in East Africa.