Data-driven Bayesian network modelling to explore the relationships between SDG 6 and the 2030 Agenda.

The Sustainable Development Goals (SDGs) are presented as integrated and indivisible. Therefore, for monitoring purposes, conventional indicator-based frameworks need to be combined with approaches that capture and describe the links and interdependencies between the Goals and their targets. In this study, we propose a data-driven Bayesian network (BN) approach to identify and interpret SDGs interlinkages.

Leaving no one behind: Evaluating access to water, sanitation and hygiene for vulnerable and marginalized groups.

Achieving equitable access to water, sanitation and hygiene (WASH) services requires paying special attention to the most disadvantaged segments of the population. Yet, despite all the progress made to evaluate the access of vulnerable and marginalized groups, important knowledge gaps still remain with respect to identifying their specific barriers and needs. At the global level, for example, the two monitoring mechanisms for SDG 6 - the Joint Monitoring Programme (JMP) and Global Analysis and Assessment of Sanitation and drinking-water (GLAAS) - face difficulties in understanding how, and to what extent, vulnerable and marginalized groups access WASH services.

Bayesian network modelling of hierarchical composite indicators.

The water, sanitation and hygiene (WaSH) sector has witnessed the development of multiple tools for multidimensional monitoring. Hierarchical and composite indicator (CI)-based conceptual frameworks provide one illustrative example. However, this approach does not address the existing interrelationship of the indicators they comprise.

Compositional data for global monitoring: The case of drinking water and sanitation.

Introduction: At a global level, access to safe drinking water and sanitation has been monitored by the Joint Monitoring Programme (JMP) of WHO and UNICEF. The methods employed are based on analysis of data from household surveys and linear regression modelling of these results over time. However, there is evidence of non-linearity in the JMP data.