Risk prediction for Women's Health and Rights in Tanzania: novel statistical methodology to target effective interventions

This programme will extend novel advances in mathematical sciences to identify, measure and rectify previously intractable humanitarian abuses related to Rights of Women (SDG5, 3.1, 5.3). The innovations established will feed directly into government supported health and education interventions in Tanzania, East Africa - a country where women continue to suffer from pernicious inequality, leading to horrendous and sustained humanitarian abuses: widespread Female Genital Mutilation (FGM), Forced Marriage and continued unacceptable rates of Perinatal Mortality.

To address these issues a new mathematical framework is required, motivated by a single key issue underpinning SDG5, and distinct from most others. Dreadful as they are, the challenges facing other SDGs, whether they be floods, disease or even extreme poverty, are visible: they can be observed, modelled, monitored. The challenge of Women's Rights abuses stand in contrast: here data is obscured, censored and hidden from sight, often intentionally so. What data does exist is partial, unrepresentative and multi-viewed, arriving piecemeal from disparate sources. As a consequence, solutions based on mathematical modelling are often passed over wholesale. Only aggregate, region level figures tend to be known. Vulnerability and risk across individuals are left unmodelled; interventions fail and abuses are perpetuated.

This destructive pathway is symptomatic of many rights issues of girls and women, and calls for a statistical approach designed specifically to handle the highly sparse, noisy and unbalanced data common to problems of this nature. Technical work to address this issue will be undertaken in three phases. First, a methodology for probabilistic data assembly will be developed to address hidden and obfuscated data challenges. This is followed by key extensions to Object Oriented and Topological Data Analysis that handle multi-view and temporally unaligned datasets. A final stage sees integration of developments into full predictive models, and the completion of the framework.

Through partnership with in-country academics, government, private-sector partners and NGOS, and application of novel data sources (digital health data; drone/earth observation imagery; mobile-money; cell network data; crowd-sourced event reporting), resulting models will be used in two key intervention streams during project lifetime: 1. Perinatal mortality modelling with partners d-tree and the Ministry of Health (SDG 3.1); 2. FGM/Forced Marriage modelling (SDG 5.3) for target educational interventions with partners onebillion, Hope for Girls and Women and the Tanzania Development Trust. While these interventions provide initial focus for mathematical sciences developments, we expect the framework generated to have application beyond this geographical extent, and to a wide range of Sustainable Development Goals.

The project will provide impact to benefit direct partner organizations, citizens, community leaders and workers, NGOs and other organisations.

In particular, the project will provide direct and ongoing societal impact aimed at reducing perinatal mortality (SDG 3.1, Intervention Package 1) and reducing incidences of FGM/Forced Marriage (SDG 5.3, Intervention Package 2). Direct beneficiaries will be vulnerable women and girls, our partner organisations (D-Tree, onebillion, Hope for Girls and Women, Tanzania Development Trust), community leaders and workers, and other NGOs. Resulting in otherwise unobtainable vulnerability and health models, the project will deliver learnt economic and societal information; this much needed knowledge will benefit and enable community leaders, NGOs and other organisations beyond our partners in the project to drive evidence based policy change.

Our partners will benefit from increased economic efficiency from implementation of the methodology and models. Longer term, economic opportunities exist for these and other data products being developed as a service with regular updates though continued collaboration with partners outside the timeframe of the project.

Underpinning this societal and economic impact, advances in knowledge in statistical methodology will be delivered (WP1-3) and through dissemination routes the impact will be driven beyond the partners in this project: to further government, commercial, parastatal and other interested organisations (including the World Bank, DFID). In addition the project will be used to drive increased awareness of the prevalence, challenges and issues both to the general public (via public outreach activities) and provide benefit for further investigations in contexts outside the scope of this grant within related multidisciplinary fields where similar problems of hidden, censored, noisy and sparsely observed data arise (e.g. food poverty - University of Nottingham Food Beacon, issues of slavery - University of Nottingham Rights Lab).

Direct impact upon the skills and capacity of researchers (2 UK RFs, 1 PhD student) and African mathematicians will be delivered as an integral part of the project. The former will build research capacity and future research leaders in this interdisciplinary field within the UK. The latter will importantly facilitate the in-country applications and refinement of the proposed solutions in addition to providing capacity for the ongoing use, maintenance and extensions of the proposed approach in the region.