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

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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.