Statistical Modelling of Malaria Parasite Clearance

The aim of the project will be to develop a robust model for parasite clearance rate, in the context of emerging antimalarial resistance. This will be based on the parasite clearance estimator method developed by Flegg et al. 2011, but also incorporate the Bayesian hierarchical regression utilised in Fogarty et al. 2015. The regression will be done in Stan via an R interface to Stan (RStan) for a patient from the Pursat dataset, and if time allows would be extended to a multi-patient model.

Meg Tully

The University of Melbourne

Meg is a student at Melbourne University completing a Bachelor of Science (majoring in Maths & Stats) with a Concurrent Diploma in Indonesian Language. She aims to utilise a strong mathematical background to tackle challenging problems in public health, ecosystem management and sustainability. Meg loves the capacity of statistics to uncover important relationships that may seem counterintuitive or unobvious without mathematical analysis.

Outside of her studies, Meg loves travel, Attenborough docos, collecting houseplants and subjecting friends to her varied attempts at cooking. She spent most of 2019 in the US for an exchange at Boston College and is always up for meeting people and exploring new places.

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