Dr Sam Jones

Research Assistant

I graduated with a BSc in Biomedical Science from the University of Warwick in 2013 where I developed  a keen interest in mathematical modelling of infectious diseases. I then gained an MSc in Epidemiology from Imperial College London in 2014, before starting work at LSTM in January 2015. I gained my PhD from LSTM in November 2019 with  a  thesis entitled “Computer modelling approaches for improving analysis of  anti-malarial clinical trials.”


Development of computer modelling methodologies with which to simulate therapeutic efficacy studies (TES) of artemisinin-based combination therapies (ACTs) for treatment of uncomplicated Falciparum malaria. TES are routinely conducted in vivo to monitor ongoing drug efficacy. However, current genotyping techniques mean that there is difficulty distinguishing between recrudescent infections, indicative of treatment failure, and reinfections that occur during follow-up in malaria endemic areas. A modelling methodology is able to accurately identify both recrudescence and reinfection. This methodology is being used to quantify the accuracy of existing and novel genotyping techniques and identify how they may be improved. This work also involves developing and maintaining modelling assets to allow all existing frontline ACTs and future novel compounds to be investigated.

Development of computer modelling methodologies to simulate artemisinin (specifically artesunate) treatment of severe malaria. Circulating parasite clearance rates are typically used to measure effectiveness of treatment in severe malaria trials, despite the large body of evidence indicating that sequestered - not circulating – parasites are primarily responsible for severe malaria pathology. Sequestered parasite load is extremely difficult to measure accurately in vivo, so development of a modelling methodology to track changes in sequestered parasite load over time can be utilized to improve insight into the impact of different drug regimens and the threat posed by artemisinin resistance to effective.

Using computer models of insecticide resistance to investigate deployment strategies of multiple insecticides and when insecticide resistance will be likely to occur with different strategies.