Clement Twumasi

Research

Research interests

Funding source

Vice Chancellor’s International Scholarship for Research Excellence Award for Doctor of Philosophy of Mathematics, Cardiff University (UK) 2018/2019

Teachings

• Probability Theory


• Statistical inference


• Time Series Analysis


• Maths Support (across all disciplines of Cardiff University)

Background of study

The management of infectious disease is a major concern for conservation of wild fish stocks and is a primary constraint on the viability and sustainability of farmed fish. One pathogen alone, infectious salmon anaemia, is estimated to have cost the Scottish farming industry £20 million in the 1998/1999 outbreak, and still costs the Norwegian and Canadian industries around US$11 and $14 million, respectively, per annum. Emerging infectious diseases also pose a serious economic risk to freshwater fisheries, with a number of newly detected pathogens causing large scale disease outbreaks in England, and, some trout fisheries in South Wales and South West England have had to close due these pathogens. Investigating the dynamics of infectious diseases among fishes is of high importance since farmed fish are the major source of human protein and aquaculture contributes significantly to the world economy.

Aims

This research focused on the dynamics of Gyrodactylus parasitic infections among fish. Quite a bit is known about the dynamics of Gyrodactylus infections on a single fish, and there exist (agent based) simulation models that reproduce these dynamics. The overarching aim of this research was to produce a simulation model for Gyrodactylus infections across a whole population of fish, realistic enough that it can be used to inform management decisions for the control of Gyrodactylus infections. The Approximate Bayesian Computation (ABC) methodology was developed for complex model calibration.  The study has motivated range of other research areas and important biological questions.

Research area

• Compartmental models (Dynamical Systems)


• Continuous-time Markov chains


• Predator-prey models and host-parasite models


• Biology of Gyrodactylus parasites


• Stochastic simulation


• Multi-state Markov Modelling


• Bayesian Inference and hypothesis testing coupled with region of practical equivalence and credible intervals


• Approximate Bayesian Computation


• Social Network Models

Teaching

• Postgraduate Tutor/ Teaching Assistance, Cardiff School of Mathematics
• Graduate Teaching Assistant
  September 2017 - September 2018
  Department of Statistics
  University of Ghana-Legon
  Duties: teach graduate courses including Stochastic Processes, Advanced Engineering Mathematics as well as Estimation and Statistical Inference. Assist other lecturers in courses at other departments involving statistics in the university.
• National Service (Research Assistant and Teaching Assistant)
  September 2015- Oct. 2016
  School of Public Health/Department of Statistics and Mathematics
  University of Ghana Accra-Legon
  Duties: taught Multivariate Methods, Stochastic Processes, Actuarial Statistics and Numeracy Skills at the Department of Statistics as a teaching assistant. Taught Financial Mathematics at the Department of Mathematics. Assisted in Projects or academic research works and taught Biostatics at the School of Public Health as TA.

Thesis

In Silico Modelling Of Parasite Dynamics

Background

The management of infectious disease is a major concern for conservation of wild fish stocks and is a primary constraint on the viability and sustainability of farmed fish. One pathogen alone, infectious salmon anaemia, is estimated to have cost the Scottish farming industry £20 million in the 1998/1999 outbreak, and still costs the Norwegian and Canadian industries around US$11 and $14 million, respectively, per annum. Emerging infectious diseases also pose a serious economic risk to freshwater fisheries, with a number of newly detected pathogens causing large scale disease outbreaks in England, and, some trout fisheries in South Wales and South West England have had to close due these pathogens. Investigating the dynamics of infectious diseases among fishes is of high importance since farmed fish are the major source of human protein and aquaculture contributes significantly to the world economy.

Abstract:

PhD Thesis URL Link: https://orca.cardiff.ac.uk/id/eprint/150632/

Understanding host-parasite systems are challenging if biologists employ just the experimental approaches adopted, whereas mathematical models can help uncover other in-depth knowledge about host infection dynamics. Previous experimental studies have explored the infrapopulation dynamics of Gyrodactylus turnbulli and G. bullatarudis ectoparasites on their fish host, Poecilia reticulata. However, other important and open biological questions exist concerning parasite microhabitat preference, host survival, parasite virulence, and the transmission dynamics of different Gyrodactylus strains across different host populations over time. This thesis mathematically investigates these relevant biological questions to understand the gyrodactylid-fish system’s complexity better using a sophisticated multi-state Markov model (MSM) and a novel individual-based stochastic simulation model. The infection dynamics of three different gyrodactylid strains are compared across three different host populations. A modified approximate Bayesian computation (ABC) with sequential Monte Carlo (SMC) and sequential importance sampling (SIS) is developed for calibrating the novel stochastic model based on existing empirical data and an auxiliary stochastic model. In addition, an extended local-linear regression (with L2 regularisation) for ABC post-processing analysis has been proposed. Advanced statistics and an MSM are used to assess spatial-temporal parasite dynamics. A linear birth-death process with catastrophic extinction (B-D-C process) is considered the auxiliary model for the complex simulation model to refine the modified ABC’s summary statistics, with other theoretical justifications and parameter estimation techniques of the B-D-C process provided. The B-D-C process simulation using τ -leaping also provides additional insights on accelerating the complex simulation model by proposing a reasonable error threshold based on the trade-off between simulation accuracy and computational speed. The mathematical models can be extended and adapted for other host-parasite systems, and the modified ABC methodologies can also aid in efficiently calibrating other multi-parameter models with a high-dimensional set of correlating or independent summary statistics.

Funding source

PhD Vice Chancellor's International Scholarship for Research Excellence

Supervisors