About

 Dr Clement Twumasi is starting a new full-time position as a Medical Statistician at the Oxford University Nuffield Department of Medicine (from 4th September, 2023). This new position at Oxford University coupled with his outstanding academic track record (within and outside the UK), has also helped him to successfully secure/receive the UK’s prestigious 3-year Global Talent Visa for Exceptional Talents. Based at the Medawar Building for Pathogen Research, he will work with Professors Eleanor Barnes, John Frater and Paul Klenerman (NDM Experimental Medicine Division) and Professor Susanna Dunachie (Tropical Medicine) on projects within the scope of the ‘Vaccines in Chronic Disease and Aging’ subtheme of the Oxford Biomedical Research Centre theme ‘Life-saving Vaccines’. As a medical statistician, he also collaborates with the Oxford University Jenner Institute (where I provide statistical consultancy services), and the Oxford Vaccine Group.

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Prior to this new position at Oxford University, Dr Twumasi was a full-time Clinical Trials Statistician at the Imperial College London School of Public Health/Imperial Clinical Trials Unit in mid-November 2022. At Imperial, he worked on world-class clinical trials on respiratory and cardiovascular infections (namely, MET-FINGER, ON-PACE, PROTECT-HF and NUC-B trials). Before joining Imperial College London, Dr Twumasi was a postdoctoral research assistant in Biostatistics at the Oxford University Statistics Department until September 2022. He completed his PhD in Mathematics at Cardiff University (from 2018 to 2021) as a Vice Chancellor's Scholar. The overarching aim of his PhD research was to develop novel mathematical models to help better understand the spread of Gyrodactylus infection dynamics of three different parasite strains across three different populations of fish. It required the development of novel agent-based stochastic simulation models and sophisticated mathematical models (including but not limited to time-inhomogeneous multistate survival Markov models and birth-death processes with catastrophic extinction) as well as Approximate Bayesian Computation (ABC) methodologies for complex likelihood-free model calibration within the Bayesian setting. Dr Twumasi is currently a reviewer for top Q1 journals (which focus on econometric modelling and machine learning predictions). This includes the International Journal of Forecasting, PLOS ONE and PLOS Computational Biology Journals, where he reviews papers on time-series predictions using state-of-the-art time series models and machine learning techniques (with specific applications to blood supply chain) as well as works on mathematical biology/infectious disease modelling.  

 

His postdoctoral position at Oxford University Statistics Department was connected to a BBSRC-funded research project developing a methodology for deep phenotyping based on latent stochastic processes, with applications in human health, genomics, and ageing. While nearing the completion of his PhD at Cardiff and before starting his postdoctoral studies in Oxford, he was first employed as a Biostatistics Research Assistant at the Oxford University Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS). The Oxford University NDORMS post focused on biostatistics research involving the mathematical modelling of musculoskeletal disorders (specifically, Metabolic bone diseases) using clinical data from observational studies and routinely collected medical records. He obtained his MPhil degree in Statistics at the University of Ghana with a brilliant academic track record, where his MPhil research was awarded the best research in mathematics in 2018 among all universities in Ghana. Dr Twumasi was selected among the 200 most exceptional mathematicians and computer scientists worldwide of their generation by international experts for the 8th Heidelberg Laureate Forum, Germany 2021 during his PhD studies.

 

Dr Clement also has expertise in statistical and mathematical modelling, which includes but is not limited to: Stochastic modelling, Multistate Survival modelling, Regression modelling (fixed and random-effect models for cross-sectional and longitudinal/panel data, respectively), Disease modelling (both deterministic and stochastic class of models), Functional Data Analysis, Machine Learning, Markov Models, Bayesian Networks, Approximate Bayesian Computation, Time series models, Credit Risk Modelling/ Credit Scorecard Building, and other general statistical analysis (Univariate and Multivariate analyses). He has a robust background in mathematics and statistics, with a wide range of mathematical modelling and computational expertise across diverse application areas. Clement has developed new interests in clinical trial studies, and one of his long-term career goals is to establish a career and expertise in trial methodologies. 

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