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

健康大数据是一个不断增长的资源和复杂的跨学科挑战。大型异构数据集的可用性将不同领域的数据联系起来-例如心理健康指标和微生物基因组学-有可能解开个人生命过程中无数元素与一般社会之间复杂的相互作用2,3。为了最大限度地发挥这一潜力，迫切需要先进的方法来分析这些复杂的数据。然而，对如此复杂的关联数据的分析提出了挑战，传统应用的统计分析常常与数据本身作斗争。数据可能是有噪声的，并且噪声是非随机的。问卷的回答往往带有系统性的偏见，某些方面的自我报告会受到回忆能力差、可取性差、偏见或不准确的自我估计的影响。数据也经常以分类形式出现，而不是数字形式，这对于某些形式的统计建模可能会有问题。最后，缺失数据，在缺失项目反应和纵向磨损方面是常见的。因此，我们需要更灵活和先进的技术来解决这些问题。在本博士和硕士学位课程中，我们将通过应用基于网络的方法（通常用于分析生物系统）来开发创新的跨学科解决方案，以分析综合社会科学和生物医学数据。贝叶斯网络有潜力解决社会科学数据面临的许多技术建模问题，但尚未在社会科学中广泛应用。这是因为在它们成为真正可用的方法之前，有必要开发实用的方法和具体的理论进展。在这里，我们将开发这些进步（下面有进一步的细节）。该项目将把BN方法应用于一项紧迫而复杂的全球卫生挑战：东非三个国家的抗菌药物耐药性（ABR）。ABR水平的增加对人类健康的潜在危害是巨大的。非洲是世界上最容易受到抗生素耐药性增加影响的区域之一，与世界其他区域相比，非洲的传染病负担最重。提出了一种“一个健康”的跨学科方法来理解这一问题，强调社会和生物领域的相互联系，涵盖人类、动物和环境领域。然而，如果没有先进的工具，很难分析如此复杂的系统。可能存在多种不同规模的驱动因素：个人行为、家庭、社区和医疗保健系统水平。然而，这些驱动因素在预测个人和社区水平的AMR水平方面的相对重要性和相互关系尚未得到很好的理解，而且可能是高度具体的背景研究问题。本博士的研究问题是双重的，方法和实质性的：1。方法：发展BN结构学习，以便整合跨社会科学和卫生领域的跨学科大数据，具体解决：datab失踪。直言variablesc。noise2抱有偏见。实质性的重点是传染病的个案研究：利用HATUA的数据，调查东非尿路感染中抗菌药物耐药性的驱动因素。

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 asystematically 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 specificResearch 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 datab. Categorical variablesc. Biased noise2. 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.