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Title: Forecasting and inventory optimisation in global health immunization programmes Supervisors: Dr Xun Wang, Dr Bahman Rostami-Tabar Background: Ministries of Health can be reluctant to procure and store more vaccines than what national forecasts indicate to be required. However, forecasts are notoriously inaccurate as they are based on population projections using often outdated demographic data. The direct consequence is low vaccine coverage, and in particular, ‘zero-dose children’, defined as children who do not receive the initial dose of diphtheria, tetanus and pertussis-containing vaccine nor any of the subsequent required vaccines. Governments and organizations are exploring alternative approaches to optimize vaccine supply chains, and looking for arguments for changing the forecasting method and for increasing the vaccine stock level. A return on investment approach as a means to increase coverage, set against the costs of not vaccinating, could potentially add weight to this argument. It is therefore necessary to understand how non-monetary measures related to wellbeing and social factors such as DALYs (disability adjusted life years) and QALYs (quality adjusted life years) could be integrated in supply chain cost functions and return on investment calculations for vaccine supply chain planning. Objectives:The overall objective is to propose new forecasting and inventory strategies to improve vaccine coverage. The PhD research would involve a series of research modules to realise the above objective: developing a new forecasting methodology to estimate zero-dose children vaccine requirement considering the lack of data and data inaccuracy using machine learning and expert judgements; exploring how and what non-monetary measures might be integrated in the vaccine supply chain cost optimisation and the relationship of DALYs/QALYs to Missed Opportunities to Vaccinate (MOVs); modelling and optimization using real data to examine return on investment using alternative forecasting methods, and how DALYs/QALYs improve/decline as vaccine stocks are adjusted. Outcomes: The key outcomes of the proposed project are A metric system of vaccine inventory management that incorporates non-monetary measures such as vaccine coverage and life expectancies A forecasting and inventory control strategy that improves the performance of inventory control with respect to the new metric Eligibility: Applicants from all nationalities are welcome. Applicants should have a strong STEM background, and have undertaken modules or have equivalent experiences in mathematical modelling, optimization and data analysis. Familiarity with a programming language, including R, Python and others, is also required. Knowledge in time-series forecasting and inventory control is desired. The supervisory team will support the applicants in applying PhD scholarships in due process. To Apply: Please send your cover letter and CV to Dr Xun Wang (wangX46@cardiff.ac.uk) or Dr Bahman Rostami-Tabar (Rostami-TabarB@cardiff.ac.uk).

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